GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera
  Control
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/
67c912b1b5903dd437cc2370
Xuanchi Ren
admin_assigned
2025-03-06T09:55:04.321Z
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Xuanchi Ren
user
xrenaa
67c912b1b5903dd437cc2371
Tianchang Shen
67c912b1b5903dd437cc2372
Jiahui Huang
67c912b1b5903dd437cc2373
Huan Ling
67c912b1b5903dd437cc2374
Yifan Lu
67c912b1b5903dd437cc2375
Merlin Nimier-David
67c912b1b5903dd437cc2376
Thomas Müller
67c912b1b5903dd437cc2377
Alexander Keller
67c912b1b5903dd437cc2378
Sanja Fidler
67c912b1b5903dd437cc2379
Jun Gao
GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera
  Control
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/
GEN3C, a generative video model, uses a 3D cache to achieve precise camera control and temporal consistency in video generation, outperforming previous methods in sparse-view novel view synthesis.
generative video model camera control temporal 3D consistency point clouds pixel-wise depth 3D cache 2D renderings camera trajectory sparse-view novel view synthesis driving scenes monocular dynamic video