{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T06:07:14Z","timestamp":1788934034446,"version":"build-2803163510"},"publisher-location":"Cham","reference-count":80,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032373687","type":"print"},{"value":"9783032373694","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-37369-4_33","type":"book-chapter","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T05:48:38Z","timestamp":1788932918000},"page":"608-627","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Video Generation Models Are General-Purpose Vision Learners"],"prefix":"10.1007","author":[{"given":"Letian","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuhan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rishabh","family":"Kabra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasper","family":"Uijlings","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steven","family":"Waslander","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrew","family":"Zisserman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joao","family":"Carreira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kaiming","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Misha","family":"Andriluka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduard Gabriel","family":"Bazavan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei","family":"Zanfir","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristian","family":"Sminchisescu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,10]]},"reference":[{"key":"33_CR1","unstructured":"Acuaviva, P., et al.: From generation to generalization: emergent few-shot learning in video diffusion models. arXiv preprint arXiv:2506.07280 (2025)"},{"key":"33_CR2","doi-asserted-by":"publisher","first-page":"61872","DOI":"10.52202\/079017-1977","volume":"37","author":"R Bachmann","year":"2024","unstructured":"Bachmann, R., et al.: 4m\u201321: an any-to-any vision model for tens of tasks and modalities. Adv. Neural. Inf. Process. Syst. 37, 61872\u201361911 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"33_CR3","doi-asserted-by":"crossref","unstructured":"Bagchi, A., Bao, Z., Wang, Y.X., Tokmakov, P., Hebert, M.: ReferEverything: towards segmenting everything we can speak of in videos. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 23221\u201323231 (2025)","DOI":"10.1109\/ICCV51701.2025.02156"},{"key":"33_CR4","unstructured":"Bardes, A., et al.: Revisiting feature prediction for learning visual representations from video. arXiv preprint arXiv:2404.08471 (2024)"},{"key":"33_CR5","unstructured":"Bazavan, E.G., Zanfir, A., Zanfir, M., Freeman, W.T., Sukthankar, R., Sminchisescu, C.: HSPACE: synthetic parametric humans animated in complex environments (2022). https:\/\/arxiv.org\/abs\/2112.12867"},{"key":"33_CR6","doi-asserted-by":"crossref","unstructured":"Bin, Y., Hu, W., Wang, H., Chen, X., Wang, B.: NormalCrafter: learning temporally consistent normals from video diffusion priors (2025). https:\/\/arxiv.org\/abs\/2504.11427","DOI":"10.1109\/ICCV51701.2025.00780"},{"key":"33_CR7","unstructured":"Blender Online Community: Blender - a 3d modeling and rendering package (2025). https:\/\/www.blender.org"},{"key":"33_CR8","first-page":"1877","volume":"33","author":"T Brown","year":"2020","unstructured":"Brown, T., et al.: Language models are few-shot learners. Adv. Neural. Inf. Process. Syst. 33, 1877\u20131901 (2020)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"33_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/978-3-642-33783-3_44","volume-title":"Computer Vision \u2013 ECCV 2012","author":"DJ Butler","year":"2012","unstructured":"Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7577, pp. 611\u2013625. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-33783-3_44"},{"key":"33_CR10","unstructured":"Carion, N., et\u00a0al.: SAM 3: segment anything with concepts. arXiv preprint arXiv:2511.16719 (2025)"},{"key":"33_CR11","unstructured":"Carnegie Mellon University: CMU Graphics Lab Motion Capture Database. http:\/\/mocap.cs.cmu.edu\/ (2001). Database created with funding from NSF EIA-0196217"},{"key":"33_CR12","unstructured":"Chen, S., Wong, S., Chen, L., Tian, Y.: Extending context window of large language models via positional interpolation (2023). https:\/\/arxiv.org\/abs\/2306.15595"},{"key":"33_CR13","doi-asserted-by":"crossref","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers), pp. 4171\u20134186 (2019)","DOI":"10.18653\/v1\/N19-1423"},{"key":"33_CR14","doi-asserted-by":"crossref","unstructured":"Ding, H., Liu, C., He, S., Jiang, X., Loy, C.C.: MeViS: a large-scale benchmark for video segmentation with motion expressions. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 2694\u20132703 (2023)","DOI":"10.1109\/ICCV51070.2023.00254"},{"key":"33_CR15","unstructured":"Esser, P., et al.: Scaling rectified flow transformers for high-resolution image synthesis. In: Forty-First International Conference on Machine Learning (2024)"},{"key":"33_CR16","doi-asserted-by":"publisher","unstructured":"Fu, X., et al.: GeoWizard: unleashing the diffusion priors for 3D geometry estimation from a single image. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) Computer Vision \u2013 ECCV 2024. LNCS, vol. 15080, pp. 241\u2013258. Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-031-72670-5_14","DOI":"10.1007\/978-3-031-72670-5_14"},{"key":"33_CR17","unstructured":"Gabeur, V., et al.: Image generators are generalist vision learners. arXiv preprint arXiv:2604.20329 (2026)"},{"key":"33_CR18","doi-asserted-by":"crossref","unstructured":"Gaidon, A., Wang, Q., Cabon, Y., Vig, E.: Virtual worlds as proxy for multi-object tracking analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4340\u20134349 (2016)","DOI":"10.1109\/CVPR.2016.470"},{"key":"33_CR19","doi-asserted-by":"crossref","unstructured":"Garcia, G.M., Zeid, K.A., Schmidt, C., De Geus, D., Hermans, A., Leibe, B.: Fine-tuning image-conditional diffusion models is easier than you think. In: Proceedings of the Winter Conference on Applications of Computer Vision (WACV), pp. 753\u2013762 (2025)","DOI":"10.1109\/WACV61041.2025.00083"},{"issue":"11","key":"33_CR20","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1177\/0278364913491297","volume":"32","author":"A Geiger","year":"2013","unstructured":"Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: the KITTI dataset. Int. J. Robot. Res. 32(11), 1231\u20131237 (2013)","journal-title":"Int. J. Robot. Res."},{"key":"33_CR21","unstructured":"He, J., Li, H., Sheng, M., Chen, Y.C.: Lotus-2: advancing geometric dense prediction with powerful image generative model. arXiv preprint arXiv:2512.01030 (2025)"},{"key":"33_CR22","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"33_CR23","unstructured":"Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models (2020). https:\/\/arxiv.org\/abs\/2006.11239"},{"key":"33_CR24","unstructured":"Hu, W., et al.: DepthCrafter: generating consistent long depth sequences for open-world videos (2024). https:\/\/arxiv.org\/abs\/2409.02095"},{"key":"33_CR25","unstructured":"Huang, J., et al.: UnityVideo: unified multi-modal multi-task learning for enhancing world-aware video generation. arXiv preprint arXiv:2512.07831 (2025)"},{"key":"33_CR26","doi-asserted-by":"crossref","unstructured":"Huang, P.H., Matzen, K., Kopf, J., Ahuja, N., Huang, J.B.: DeepMVS: learning multi-view stereopsis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2821\u20132830 (2018)","DOI":"10.1109\/CVPR.2018.00298"},{"key":"33_CR27","doi-asserted-by":"crossref","unstructured":"Jiang, Z., Zheng, C., Laina, I., Larlus, D., Vedaldi, A.: Geo4D: leveraging video generators for geometric 4D scene reconstruction. arXiv preprint arXiv:2504.07961 (2025)","DOI":"10.1109\/ICCV51701.2025.01921"},{"key":"33_CR28","doi-asserted-by":"crossref","unstructured":"Kaufmann, M., et al.: EMDB: the electromagnetic database of global 3D human pose and shape in the wild. In: International Conference on Computer Vision (ICCV) (2023)","DOI":"10.1109\/ICCV51070.2023.01345"},{"key":"33_CR29","doi-asserted-by":"crossref","unstructured":"Ke, B., Obukhov, A., Huang, S., Metzger, N., Daudt, R.C., Schindler, K.: Repurposing diffusion-based image generators for monocular depth estimation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9492\u20139502 (2024)","DOI":"10.1109\/CVPR52733.2024.00907"},{"key":"33_CR30","doi-asserted-by":"crossref","unstructured":"Ke, Z., Sun, J., Li, K., Yan, Q., Lau, R.W.: MODNet: real-time trimap-free portrait matting via objective decomposition. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 1140\u20131147 (2022)","DOI":"10.1609\/aaai.v36i1.19999"},{"key":"33_CR31","doi-asserted-by":"crossref","unstructured":"Keetha, N., et al.: MapAnything: universal feed-forward metric 3D reconstruction. arXiv preprint arXiv:2509.13414 (2025)","DOI":"10.1109\/3DV69130.2026.00054"},{"key":"33_CR32","doi-asserted-by":"publisher","first-page":"206","DOI":"10.1007\/978-3-031-73235-5_12","volume-title":"Computer Vision - ECCV 2024","author":"R Khirodkar","year":"2025","unstructured":"Khirodkar, R., et al.: Sapiens: foundation for human vision models. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) Computer Vision - ECCV 2024, pp. 206\u2013228. Springer Nature Switzerland, Cham (2025)"},{"key":"33_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"123","DOI":"10.1007\/978-3-030-20870-7_8","volume-title":"Computer Vision \u2013 ACCV 2018","author":"A Khoreva","year":"2019","unstructured":"Khoreva, A., Rohrbach, A., Schiele, B.: Video object segmentation with language referring expressions. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11364, pp. 123\u2013141. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20870-7_8"},{"key":"33_CR34","unstructured":"Kingma, D.P.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"33_CR35","unstructured":"Kirillov, A., et al.: Segment anything. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 4015\u20134026 (2023)"},{"key":"33_CR36","unstructured":"Kuang, Z., et al.: Buffer anytime: zero-shot video depth and normal from image priors (2024). https:\/\/arxiv.org\/abs\/2411.17249"},{"key":"33_CR37","unstructured":"Li, J., et al.: GENMO: generative models for human motion synthesis. arXiv preprint arXiv:2505.01425 (2025)"},{"key":"33_CR38","unstructured":"Li, T., He, K.: Back to basics: let denoising generative models denoise. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 36115\u201336125 (2026)"},{"key":"33_CR39","unstructured":"Liang, R., et al.: DiffusionRenderer: neural inverse and forward rendering with video diffusion models (2025). https:\/\/arxiv.org\/abs\/2501.18590"},{"key":"33_CR40","unstructured":"Lin, H., et al.: Depth anything 3: recovering the visual space from any views. arXiv preprint arXiv:2511.10647 (2025)"},{"key":"33_CR41","doi-asserted-by":"crossref","unstructured":"Lin, S., Ryabtsev, A., Sengupta, S., Curless, B.L., Seitz, S.M., Kemelmacher-Shlizerman, I.: Real-time high-resolution background matting. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 8762\u20138771 (2021)","DOI":"10.1109\/CVPR46437.2021.00865"},{"key":"33_CR42","doi-asserted-by":"crossref","unstructured":"Lin, S., Yang, L., Saleemi, I., Sengupta, S.: Robust high-resolution video matting with temporal guidance. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 238\u2013247 (2022)","DOI":"10.1109\/WACV51458.2022.00319"},{"key":"33_CR43","unstructured":"Lipman, Y., Chen, R.T., Ben-Hamu, H., Nickel, M., Le, M.: Flow matching for generative modeling. arXiv preprint arXiv:2210.02747 (2022)"},{"key":"33_CR44","unstructured":"Liu, X., Gong, C., Liu, Q.: Flow straight and fast: learning to generate and transfer data with rectified flow. arXiv preprint arXiv:2209.03003 (2022)"},{"key":"33_CR45","doi-asserted-by":"publisher","first-page":"83008","DOI":"10.52202\/079017-2640","volume":"37","author":"J Martinez","year":"2024","unstructured":"Martinez, J., et al.: Codec avatar studio: paired human captures for complete, driveable, and generalizable avatars. Adv. Neural. Inf. Process. Syst. 37, 83008\u201383023 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"33_CR46","doi-asserted-by":"publisher","first-page":"58363","DOI":"10.52202\/075280-2544","volume":"36","author":"D Mizrahi","year":"2023","unstructured":"Mizrahi, D., et al.: 4m: massively multimodal masked modeling. Adv. Neural. Inf. Process. Syst. 36, 58363\u201358408 (2023)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"33_CR47","unstructured":"Oquab, M., et al.: DINOv2: learning robust visual features without supervision. arXiv preprint arXiv:2304.07193 (2023)"},{"key":"33_CR48","doi-asserted-by":"crossref","unstructured":"Pathak, D., Krahenbuhl, P., Donahue, J., Darrell, T., Efros, A.A.: Context encoders: feature learning by inpainting. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2536\u20132544 (2016)","DOI":"10.1109\/CVPR.2016.278"},{"key":"33_CR49","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Pont-Tuset, J., McWilliams, B., Van\u00a0Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 724\u2013732 (2016)","DOI":"10.1109\/CVPR.2016.85"},{"key":"33_CR50","unstructured":"Radford, A., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763. PmLR (2021)"},{"issue":"3","key":"33_CR51","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1109\/TPAMI.2020.3019967","volume":"44","author":"R Ranftl","year":"2020","unstructured":"Ranftl, R., Lasinger, K., Hafner, D., Schindler, K., Koltun, V.: Towards robust monocular depth estimation: mixing datasets for zero-shot cross-dataset transfer. IEEE Trans. Pattern Anal. Mach. Intell. 44(3), 1623\u20131637 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"33_CR52","unstructured":"Ravi, N., et\u00a0al.: SAM 2: segment anything in images and videos. arXiv preprint arXiv:2408.00714 (2024)"},{"key":"33_CR53","unstructured":"Renderpeople: Renderpeople: 3d people for renderings. https:\/\/renderpeople.com"},{"key":"33_CR54","doi-asserted-by":"crossref","unstructured":"Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 10684\u201310695 (2022)","DOI":"10.1109\/CVPR52688.2022.01042"},{"key":"33_CR55","unstructured":"Saleh, F., et al.: DAViD: data-efficient and accurate vision models from synthetic data (2025). https:\/\/arxiv.org\/abs\/2507.15365"},{"key":"33_CR56","doi-asserted-by":"crossref","unstructured":"Schops, T., et al.: A multi-view stereo benchmark with high-resolution images and multi-camera videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3260\u20133269 (2017)","DOI":"10.1109\/CVPR.2017.272"},{"key":"33_CR57","doi-asserted-by":"crossref","unstructured":"Shen, Z., et al.: World-grounded human motion recovery via gravity-view coordinates. In: SIGGRAPH Asia Conference Proceedings (2024)","DOI":"10.1145\/3680528.3687565"},{"key":"33_CR58","unstructured":"Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. In: International Conference on Learning Representations (2021)"},{"key":"33_CR59","doi-asserted-by":"publisher","first-page":"10078","DOI":"10.52202\/068431-0732","volume":"35","author":"Z Tong","year":"2022","unstructured":"Tong, Z., Song, Y., Wang, J., Wang, L.: VideoMAE: masked autoencoders are data-efficient learners for self-supervised video pre-training. Adv. Neural. Inf. Process. Syst. 35, 10078\u201310093 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"33_CR60","unstructured":"Uijlings, J., et al.: VoCap: video object captioning and segmentation from any prompt. arXiv preprint arXiv:2508.21809 (2025)"},{"key":"33_CR61","unstructured":"Wan, T.: Wan: Open and advanced large-scale video generative models. arXiv preprint arXiv:2503.20314 (2025)"},{"key":"33_CR62","doi-asserted-by":"crossref","unstructured":"Wang, J., Chen, M., Karaev, N., Vedaldi, A., Rupprecht, C., Novotny, D.: VGGT: visual geometry grounded transformer. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 5294\u20135306 (2025)","DOI":"10.1109\/CVPR52734.2025.00499"},{"key":"33_CR63","unstructured":"Wang, J., et al.: VGGT-$$omega$$. arXiv preprint arXiv:2605.15195 (2026)"},{"key":"33_CR64","unstructured":"Wang, L., Zanfir, A., Bazavan, E.G., Andriluka, M., Sminchisescu, C.: THFM: a unified video foundation model for 4D human perception and beyond. arXiv preprint arXiv:2603.25892 (2026)"},{"key":"33_CR65","doi-asserted-by":"crossref","unstructured":"Wang, W., et al.: TartanAir: a dataset to push the limits of visual SLAM. In: 2020 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4909\u20134916. IEEE (2020)","DOI":"10.1109\/IROS45743.2020.9341801"},{"key":"33_CR66","doi-asserted-by":"publisher","unstructured":"Wang, Y., Wang, Z., Liu, L., Daniilidis, K.: TRAM: global trajectory and motion of 3D humans from in-the-wild videos. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds.) Computer Vision \u2013 ECCV 2024. LNCS, vol. 15069, pp. 467\u2013487. Springer, Cham (2025). https:\/\/doi.org\/10.1007\/978-3-031-73247-8_27","DOI":"10.1007\/978-3-031-73247-8_27"},{"key":"33_CR67","unstructured":"Wiedemer, T., et al.: Video models are zero-shot learners and reasoners. arXiv preprint arXiv:2509.20328 (2025)"},{"key":"33_CR68","unstructured":"Xu, G., et al.: What matters when repurposing diffusion models for general dense perception tasks? arXiv preprint arXiv:2403.06090 (2024)"},{"key":"33_CR69","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1007\/978-3-030-01228-1_36","volume-title":"Computer Vision \u2013 ECCV 2018","author":"N Xu","year":"2018","unstructured":"Xu, N., et al.: YouTube-VOS: sequence-to-sequence video object segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 603\u2013619. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01228-1_36"},{"key":"33_CR70","unstructured":"Yang, H., et al.: Depth any video with scalable synthetic data (2025). https:\/\/arxiv.org\/abs\/2410.10815"},{"key":"33_CR71","doi-asserted-by":"crossref","unstructured":"Yang, L., Kang, B., Huang, Z., Xu, X., Feng, J., Zhao, H.: Depth anything: unleashing the power of large-scale unlabeled data. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 10371\u201310381 (2024)","DOI":"10.1109\/CVPR52733.2024.00987"},{"key":"33_CR72","doi-asserted-by":"publisher","first-page":"21875","DOI":"10.52202\/079017-0688","volume":"37","author":"L Yang","year":"2024","unstructured":"Yang, L., et al.: Depth anything v2. Adv. Neural. Inf. Process. Syst. 37, 21875\u201321911 (2024)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"33_CR73","doi-asserted-by":"crossref","unstructured":"Yang, P., Zhou, S., Zhao, J., Tao, Q., Loy, C.C.: MatAnyone: stable video matting with consistent memory propagation. In: Proceedings of the Computer Vision and Pattern Recognition Conference, pp. 7299\u20137308 (2025)","DOI":"10.1109\/CVPR52734.2025.00684"},{"key":"33_CR74","doi-asserted-by":"crossref","unstructured":"Yin, Y., Guo, C., Kaufmann, M., Zarate, J., Song, J., Hilliges, O.: Hi4D: 4D instance segmentation of close human interaction. In: Computer Vision and Pattern Recognition (CVPR) (2023)","DOI":"10.1109\/CVPR52729.2023.01632"},{"key":"33_CR75","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-319-46475-6_5","volume-title":"Computer Vision \u2013 ECCV 2016","author":"L Yu","year":"2016","unstructured":"Yu, L., Poirson, P., Yang, S., Berg, A.C., Berg, T.L.: Modeling context in referring expressions. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9906, pp. 69\u201385. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46475-6_5"},{"key":"33_CR76","doi-asserted-by":"crossref","unstructured":"Zhai, X., Mustafa, B., Kolesnikov, A., Beyer, L.: Sigmoid loss for language image pre-training. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 11975\u201311986 (2023)","DOI":"10.1109\/ICCV51070.2023.01100"},{"key":"33_CR77","unstructured":"Zhang, C., et al.: Efficiently reconstructing dynamic scenes one D4RT at a time. arXiv preprint arXiv:2512.08924 (2025)"},{"key":"33_CR78","doi-asserted-by":"crossref","unstructured":"Zhao, C., et al.: DICEPTION: a generalist diffusion model for visual perceptual tasks. arXiv preprint arXiv:2502.17157 (2025)","DOI":"10.52202\/085713-1339"},{"key":"33_CR79","doi-asserted-by":"crossref","unstructured":"Zhu, X., et al.: Uni-perceiver: pre-training unified architecture for generic perception for zero-shot and few-shot tasks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16804\u201316815 (2022)","DOI":"10.1109\/CVPR52688.2022.01630"},{"key":"33_CR80","unstructured":"Zoran, D., Parthasarathy, N., Yang, Y., Hudson, D.A., Carreira, J., Zisserman, A.: Recurrent video masked autoencoders. arXiv preprint arXiv:2512.13684 (2025)"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2026"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-37369-4_33","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T05:48:52Z","timestamp":1788932932000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-37369-4_33"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032373687","9783032373694"],"references-count":80,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-37369-4_33","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"10 September 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Malm\u00f6","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Sweden","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 September 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 September 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv.ecva.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}