title: GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera
    Control
summary: We present GEN3C, a generative video model with precise Camera Control and
  temporal 3D Consistency. Prior video models already generate realistic videos,
  but they tend to leverage little 3D information, leading to inconsistencies,
  such as objects popping in and out of existence. Camera control, if implemented
  at all, is imprecise, because camera parameters are mere inputs to the neural
  network which must then infer how the video depends on the camera. In contrast,
  GEN3C is guided by a 3D cache: point clouds obtained by predicting the
  pixel-wise depth of seed images or previously generated frames. When generating
  the next frames, GEN3C is conditioned on the 2D renderings of the 3D cache with
  the new camera trajectory provided by the user. Crucially, this means that
  GEN3C neither has to remember what it previously generated nor does it have to
  infer the image structure from the camera pose. The model, instead, can focus
  all its generative power on previously unobserved regions, as well as advancing
  the scene state to the next frame. Our results demonstrate more precise camera
  control than prior work, as well as state-of-the-art results in sparse-view
  novel view synthesis, even in challenging settings such as driving scenes and
  monocular dynamic video. Results are best viewed in videos. Check out our
  webpage! https://research.nvidia.com/labs/toronto-ai/GEN3C/
paper_authors._id: 67c912b1b5903dd437cc2370
paper_authors.name: Xuanchi Ren
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-06T09:55:04.321Z
paper_authors._id: 658529d61c461dfe88afe8e8
paper_authors.avatarUrl: /avatars/a22c1b07d28c2662833c462c6537d835.svg
paper_authors.fullname: Xuanchi Ren
paper_authors.type: user
paper_authors.user: xrenaa
paper_authors._id: 67c912b1b5903dd437cc2371
paper_authors.name: Tianchang Shen
paper_authors._id: 67c912b1b5903dd437cc2372
paper_authors.name: Jiahui Huang
paper_authors._id: 67c912b1b5903dd437cc2373
paper_authors.name: Huan Ling
paper_authors._id: 67c912b1b5903dd437cc2374
paper_authors.name: Yifan Lu
paper_authors._id: 67c912b1b5903dd437cc2375
paper_authors.name: Merlin Nimier-David
paper_authors._id: 67c912b1b5903dd437cc2376
paper_authors.name: Thomas Müller
paper_authors._id: 67c912b1b5903dd437cc2377
paper_authors.name: Alexander Keller
paper_authors._id: 67c912b1b5903dd437cc2378
paper_authors.name: Sanja Fidler
paper_authors._id: 67c912b1b5903dd437cc2379
paper_authors.name: Jun Gao
paper_title: GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera
    Control
paper_summary: We present GEN3C, a generative video model with precise Camera Control and
  temporal 3D Consistency. Prior video models already generate realistic videos,
  but they tend to leverage little 3D information, leading to inconsistencies,
  such as objects popping in and out of existence. Camera control, if implemented
  at all, is imprecise, because camera parameters are mere inputs to the neural
  network which must then infer how the video depends on the camera. In contrast,
  GEN3C is guided by a 3D cache: point clouds obtained by predicting the
  pixel-wise depth of seed images or previously generated frames. When generating
  the next frames, GEN3C is conditioned on the 2D renderings of the 3D cache with
  the new camera trajectory provided by the user. Crucially, this means that
  GEN3C neither has to remember what it previously generated nor does it have to
  infer the image structure from the camera pose. The model, instead, can focus
  all its generative power on previously unobserved regions, as well as advancing
  the scene state to the next frame. Our results demonstrate more precise camera
  control than prior work, as well as state-of-the-art results in sparse-view
  novel view synthesis, even in challenging settings such as driving scenes and
  monocular dynamic video. Results are best viewed in videos. Check out our
  webpage! https://research.nvidia.com/labs/toronto-ai/GEN3C/
paper_ai_keywords: generative video model precise Camera Control temporal 3D Consistency pixel-wise depth 3D cache point clouds 2D renderings camera parameters camera pose sparse-view novel view synthesis monocular dynamic video