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The elements correspond to geometry and material definitions of scene objects and constitute the leaves of the graph; we store them as high-dimensional vectors. The position and appearance of scene objects can be adjusted in an artist-friendly manner via familiar transformations, e.g. translation, bending, or color hue shift, which are stored in the inner nodes of the graph. In order to apply a (non-linear) transformation to a learned vector, we adopt the concept of linearizing a problem by lifting it into higher dimensions: we first encode the transformation into a high-dimensional matrix and then apply it by standard matrix-vector multiplication. The transformations are encoded using neural networks. We render the scene graph using a streaming neural renderer, which can handle graphs with a varying number of objects, and thereby facilitates scalability. Our results demonstrate a precise control over the learned object representations in a number of animated 2D and 3D scenes. Despite the limited visual complexity, our work presents a step towards marrying traditional editing mechanisms with learned representations, and towards high-quality, controllable neural rendering.<\/jats:p>","DOI":"10.1145\/3450626.3459848","type":"journal-article","created":{"date-parts":[[2021,7,20]],"date-time":"2021-07-20T00:04:26Z","timestamp":1626739466000},"page":"1-11","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":13,"title":["Neural scene graph rendering"],"prefix":"10.1145","volume":"40","author":[{"given":"Jonathan","family":"Granskog","sequence":"first","affiliation":[{"name":"NVIDIA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Till N.","family":"Schnabel","sequence":"additional","affiliation":[{"name":"NVIDIA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fabrice","family":"Rousselle","sequence":"additional","affiliation":[{"name":"NVIDIA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jan","family":"Nov\u00e1k","sequence":"additional","affiliation":[{"name":"NVIDIA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,7,19]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58542-6_42"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00466"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1553374.1553380"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/2343483.2343493"},{"key":"e_1_2_2_5_1","volume-title":"Mitra","author":"Chen Xuelin","year":"2021","unstructured":"Xuelin Chen , Daniel Cohen-Or , Baoquan Chen , and Niloy J . 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