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Training-free Camera Control for Video Generation

Chen Hou, Zhibo Chen

arXiv:2406.10126v4cs.CV

TL;DR

Most video diffusion models lack camera control, and existing approaches may require camera-annotated data or additional training. CamTrol models camera motion in 3D point-cloud space, then uses noisy-latent layout priors to guide generation. The paper reports superior video-generation quality and motion alignment compared with fine-tuned methods, alongside generalization across base models and applications to varied motion settings.

  • Problem

    Most video generation models lack camera control, while limited camera annotations and fine-tuning constraints make the task challenging.

    Source: §1 · p. 1

  • Method

    CamTrol models camera motion in 3D point-cloud space, then uses noisy-latent layout priors to guide video generation without additional fine-tuning or self-supervised training.

    Source: §1 · p. 2, §3.2 · p. 5

  • Results

    CamTrol achieves superior generation quality and motion alignment compared with other state-of-the-art methods, and remains effective across alternative base models, resolutions, and generation lengths.

    Source: §4.2 · p. 7, §4.3 · p. 9, §5 · p. 10

  • Takeaways & Limitations

    The demonstrated applications include controllable camera-motion scales, complicated trajectories, and unsupervised 3D video generation.

    Source: §4.4 · p. 10, §5 · p. 10

Abstract

from arXiv · show

We propose a training-free and robust solution to offer camera movement control for off-the-shelf video diffusion models. Unlike previous work, our method does not require any supervised finetuning on camera-annotated datasets or self-supervised training via data augmentation. Instead, it can be plug-and-play with most pretrained video diffusion models and generate camera-controllable videos with a single image or text prompt as input. The inspiration for our work comes from the layout prior that intermediate latents encode for the generated results, thus rearranging noisy pixels in them will cause the output content to relocate as well. As camera moving could also be seen as a type of pixel rearrangement caused by perspective change, videos can be reorganized following specific camera motion if their noisy latents change accordingly. Building on this, we propose CamTrol, which enables robust camera control for video diffusion models. It is achieved by a two-stage process. First, we model image layout rearrangement through explicit camera movement in 3D point cloud space. Second, we generate videos with camera motion by leveraging the layout prior of noisy latents formed by a series of rearranged images. Extensive experiments have demonstrated its superior performance in both video generation and camera motion alignment compared with other finetuned methods. Furthermore, we show the capability of CamTrol to generalize to various base models, as well as its impressive applications in scalable motion control, dealing with complicated trajectories and unsupervised 3D video generation. Videos available at https://lifedecoder.github.io/CamTrol/.

1 INTRODUCTION

Most video generators offer little camera control, while data scarcity and fine-tuning constraints limit existing approaches. CamTrol uses a training-free, two-stage approach that leverages noisy-latent layout priors to guide camera motion.

1 INTRODUCTION

  • Most video generation models produce dynamic content but fail to provide control over the generated videos’ camera motion.The introduction notes progress in generating dynamic objects and backgrounds, but says most models lack camera control.Source: §1 · p. 1
  • Camera control is constrained by sparse camera annotations and by fine-tuning methods’ resource demands and limited generalizability across scenes.Data augmentation approaches described here handle simple motions such as zoom or truck but struggle with more complicated ones.Source: §1 · pp. 1–2
  • Two observations motivate CamTrol: pretrained models can respond roughly to camera-related prompts, and video models have shown strength in 3D generation involving perspective changes.Prompted motion may be inaccurate or static, but these observations suggest pretrained models contain relevant camera-motion and perspective-change priors.Source: §1 · p. 2
  • CamTrol provides training-free camera control for off-the-shelf video diffusion models by modeling camera motion in 3D point clouds and guiding generation with noisy-latent layout priors.The method does not require fine-tuning on camera-annotated datasets or self-supervised training via data augmentation.Source: §1 · p. 2

2 RELATED WORK

Prior work on video camera control includes flow-based warping, self-supervised augmentation, and fine-tuning, while diffusion noise priors support controllable image and video generation. Pretrained video models also contribute to 3D generation through their capacity to handle camera-pose changes.

Camera Control for Video Generation

  • Video camera-control approaches include optical-flow warping, self-supervised augmentation for simple camera motion, and fine-tuning camera encoders or motion patterns.The cited approaches include adding camera encoders to temporal attention layers and learning motion patterns with LoRA and fine-tuning.Source: §2 · p. 2

Noise Prior of Latents in Diffusion Model

  • Diffusion noise priors have been used for image translation, pixel manipulation, inpainting, semantic editing, temporal consistency, and fidelity–diversity trade-offs.Initial noise can influence generated layout despite being sampled from a Gaussian distribution.Source: §2 · pp. 2–3

Video Model for 3D Generation

  • Pretrained video models support 3D generation through fine-tuning on rendered videos or camera embeddings, and have demonstrated consistent multi-view rendering of 3D objects.These results indicate prior knowledge for handling camera-pose changes.Source: §2 · p. 3

3 TRAINING-FREE CAMERA CONTROL FOR VIDEO GENERATION

CamTrol models camera motion through 3D point-cloud views, then uses inverted noisy latents to guide video diffusion. Its inversion strength balances motion fidelity against video diversity.

3.1 CAMERA MOTION MODELING

  • A single input image is lifted to a point cloud, re-rendered along explicit camera extrinsics, then inpainted and depth-aligned across overlapping views.This supports basic and hybrid motions, including trajectories specified by precise extrinsics.Source: §3.1 · pp. 3–4

3.2 LAYOUT PRIOR OF NOISE

  • Diffusion inversion of rendered camera-view images constructs noisy latents that encode the intended motion; shared pixels correlate latents, while added noise can complete blank or flawed regions.Source: §3.2 · p. 5
  • The inversion timestep t0 trades motion fidelity against video diversity: smaller values favor guidance, while larger values favor more dynamic, rational videos.The authors report smaller t0 for moderate motions and larger t0 for more drastic movements.Source: §3.2 · p. 5
  • CamTrol attains video-generation quality and camera-motion alignment comparable to finetuned methods.Source: Table 1 · p. 6
  • CamTrol follows complex zoom, pan, and roll trajectories, while finetuned comparisons miss subtle camera motions.Source: Figure 3 · p. 6

4 EXPERIMENTS

Experiments report that CamTrol combines camera-motion accuracy with video quality. Qualitative tests and applications cover generalization, controllable motion scales, and complex trajectories.

4.1 EXPERIMENTAL SETTINGS

  • SVD-based comparisons use 14 frames and 25 steps; 500 RealEstate10k prompt-trajectory pairs inform quality metrics, while ParticleSFM-derived ATE and RPE assess motion.Source: §4.1 · p. 6

4.2 COMPARISONS WITH STATE-OF-THE-ART METHODS

  • CamTrol matches finetuned methods on video quality, achieves the lowest ATE and RPE-T/R, and follows complex trajectories better qualitatively.The qualitative comparison also reports that finetuned baselines miss subtle camera motions.Source: §4.2 · p. 7
  • CamTrol preserves base-model dynamics and generalizes to broader scenes, while finetuned baselines can miss subtle motions and struggle beyond static real-estate scenes.Source: §4.2 · p. 7, Figure 4 · p. 7
  • CamTrol adds no inference GPU memory over the base model and saves 10–20GB versus other methods, allowing it to run on one RTX 3090.The computational comparison reports maximum GPU memory and processing time under unified settings.Source: §4.2 · pp. 7–8, Table 2 · p. 7

4.3 ABLATION STUDY

  • Prompting alone fails to produce correct camera motion; layout-prior guidance fills point-cloud voids and restores video dynamics.Source: §4.3 · p. 8, Figure 6 · p. 8
  • Smaller t0 improves adherence to camera-motion requirements but reduces dynamics, whereas larger t0 produces more plausible videos with weaker motion control.The reported trade-off reflects greater randomness in latents at larger timesteps.Source: §4.3 · p. 9, Figure 7 · p. 9

4.4 FURTHER APPLICATIONS

5 CONCLUSION

CamTrol provides training-free camera control for off-the-shelf video diffusion models through explicit 3D camera-motion modeling and noisy-latent layout priors. The paper reports stronger generation quality and motion alignment than other state-of-the-art methods, alongside generalization and applications including scalable control, complicated trajectories, and unsupervised 3D video generation.

5 CONCLUSION

  • CamTrol combines explicit camera-motion modeling in 3D point-cloud space with video generation guided by noisy-latent layout priors.It is training-free and requires neither camera-annotated-dataset finetuning nor self-supervised training via data augmentation.Source: §5 · p. 10
  • Comprehensive experiments show superior generation quality and motion alignment against other state-of-the-art methods.Source: §5 · p. 10
  • CamTrol generalizes to various scenarios and is applied to scalable motion control, complicated camera trajectories, and unsupervised 3D video generation.Source: §5 · p. 10

A MORE RESULTS ON CAMERA CONTROL

The supplementary results include videos with basic, hybrid, and complicated camera motions, plus unsupervised 3D rotations of objects and scenes. They also present camera-scale control and comparisons of layout-prior guidance with the base model's raw output.

A MORE RESULTS ON CAMERA CONTROL

  • The demo includes basic motions, sequential or simultaneous hybrid motions, and complicated motions generated from precise camera extrinsics, including extrinsics extracted from RealEstate10k.Hybrid examples include Zoom In followed by Pedestal Up and combined Zoom Out, Pedestal Up, Truck Left, Tilt Down, and Pan Right.Source: A MORE RESULTS ON CAMERA CONTROL · p. 14
  • Unsupervised 3D-rotation videos cover objects and scenes, with appropriate dynamics from arbitrary input images rather than the specific styles and static-object settings of trained 3D models.The paper describes this approach as potentially an infinite source of 3D data.Source: A MORE RESULTS ON CAMERA CONTROL · p. 14
  • Additional ablation results examine camera control at different scales and layout-prior guidance against the video base model's raw output.Source: A MORE RESULTS ON CAMERA CONTROL · p. 14

B DEFINITIONS OF BASIC CAMERA MOTIONS

The appendix adopts cinematography terminology for camera motions and describes how CamTrol represents and accepts trajectories. It also directs readers to supplementary visual comparisons with state-of-the-art approaches.

B DEFINITIONS OF BASIC CAMERA MOTIONS

  • The appendix uses cinematography terminology for camera-motion types and points to Table 5 for their definitions.Source: B DEFINITIONS OF BASIC CAMERA MOTIONS · p. 14
  • Camera motion is represented as a trajectory of extrinsic matrices, each comprising rotation and translation matrices for camera pose and position.Hybrid motion can combine basic motions spatially or concatenate them temporally; complicated trajectories can use precise extrinsic parameters or a reference video whose motion is estimated with SFM algorithms.Source: B DEFINITIONS OF BASIC CAMERA MOTIONS · p. 14

C VISUAL COMPARISONS WITH STATE-OF-THE-ARTS

D IMPLEMENTATION DETAILS

The implementation details specify the image-generation, inpainting, depth-estimation, video, and computational settings. The section identifies the scope of the computational analysis and a resolution limitation for SVD-based CameraCtrl.

D IMPLEMENTATION DETAILS

  • Text-prompt inputs use Stable Diffusion v2-1 or XL for initial images, Runway's Stable Diffusion inpainting model with 25 backward steps, and ZeoDepth for depth estimation.Classifier-free guidance follows each base model's default setting.Source: D IMPLEMENTATION DETAILS · p. 14
  • Table 5 gives definitions of camera motions using terminology adopted from cinematography.Source: Table 5 · p. 15
  • Video generation and reference-video processing for FVD and FID use 6 fps.This is SVD's default video-generation setting.Source: D IMPLEMENTATION DETAILS · p. 15
  • Computational analysis uses 14 frames, decoding size 14, and 25 generation steps without xformers; the analysis is reported at 576 × 1024 resolution.SVD-based CameraCtrl supports only 320 × 576 resolution.Source: D IMPLEMENTATION DETAILS · p. 15, Table 4 · p. 15

E CHOICE OF 3D REPRESENTATION

CamTrol uses point clouds as rough layout guidance because they can be built from a single image and avoid the optimization and multi-view inputs required by 3D Gaussian Splatting. This keeps the method training-free while providing sufficiently useful rendered layouts for camera-motion video generation.

E CHOICE OF 3D REPRESENTATION

  • Point clouds construct a scene from one image using depth estimation and inpainting, whereas 3D Gaussian Splatting requires optimization on images from multiple views.The point-cloud approach can therefore use 2D inpainting to gradually complete a scene from a single image.Source: E CHOICE OF 3D REPRESENTATION · p. 15
  • Point clouds lift 2D points into 3D directly, while 3D Gaussian Splatting requires per-scenario optimization and would take more time in this application.The authors say video generation takes almost no time after multi-view images are acquired with their approach.Source: E CHOICE OF 3D REPRESENTATION · p. 16
  • Point clouds provide rough layout guidance quickly, without further optimization or redundant multi-view inputs, supporting training-free video generation from a single image or text prompt.The section’s goal is rough layout guidance, not precise 3D reconstruction, so rendered point-cloud images are considered sufficient without refinement.Source: E CHOICE OF 3D REPRESENTATION · p. 16

F DETAILS ABOUT GENERATING 3D ROTATION VIDEOS

For unsupervised 3D video generation, CamTrol uses conventions for unknown camera parameters and estimates camera-to-content distance from a central image patch. It then forms camera rotations using stepwise y-axis angles.

F DETAILS ABOUT GENERATING 3D ROTATION VIDEOS

  • CamTrol uses video-foundation-model priors to generate dynamic 3D objects and scenes without training or regulated 3D datasets.The authors say those priors include scenario diversity, moving-object dynamics, and temporal consistency.Source: F DETAILS ABOUT GENERATING 3D ROTATION… · p. 16
  • For wild-image inputs, the camera intrinsic matrix K and initial extrinsic matrix P0 are set by convention because they are usually intractable.The camera-to-content distance f is estimated as the average depth in a patch around the input image’s center; the experiment uses a 21-by-21 patch.Source: F DETAILS ABOUT GENERATING 3D ROTATION… · pp. 16–17
  • At step i, θi specifies rotation around the y axis, and the camera rotation and translation matrices are formed for the motion.The passage defines D as the image’s depth estimate and P as the patch around the center point.Source: F DETAILS ABOUT GENERATING 3D ROTATION… · p. 17

G MOTION BLUR, PROBLEMS AND SOLUTIONS

Large perspective changes can produce visible trails or motion blur, especially in short videos. The section describes ways to mitigate this through smaller moves, longer generations, chained generations, interpolation, or higher frame rates, each with relevant conditions or trade-offs.

G MOTION BLUR, PROBLEMS AND SOLUTIONS

  • Drastic perspective changes can cause visible trails or motion blur, especially with short generation lengths such as 16 frames and large moves such as a tilt of 60 degrees or more.The authors attribute the blur issue to limited generation frames and large camera movements.Source: G MOTION BLUR, PROBLEMS AND SOLUTIONS · p. 17
  • For severe perspective changes, reducing motion scale or using a model with longer generation lengths can alleviate blur.The section gives CogVideoX’s 49-frame generation capability as an example of a longer generation length.Source: G MOTION BLUR, PROBLEMS AND SOLUTIONS · p. 17
  • Chaining generations by starting each new I2V generation from the previous last frame can form a longer output; for T2V models, increasing camera-motion inversion step t0 is another option.The section says increasing t0 guarantees more fidelity toward input-image content and motion; frame interpolation is also offered as a convenient option.Source: G MOTION BLUR, PROBLEMS AND SOLUTIONS · p. 17
  • When video length cannot change, increasing frame rate can reduce visually perceived blur, but shortens video duration and makes motion persistence less pronounced.The passage presents the frame-rate change as a visual-quality trade-off.Source: G MOTION BLUR, PROBLEMS AND SOLUTIONS · p. 17
  • The experiments use raw outputs in all settings.Source: G MOTION BLUR, PROBLEMS AND SOLUTIONS · p. 17

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