PaperlayerView exact revision on arXiv

Source-linked AI summary

VideoControlNet: A Motion-Guided Video-to-Video Translation Framework by Using Diffusion Model with ControlNet

Zhihao Hu, Dong Xu

arXiv:2307.14073v2cs.CV

TL;DR

Diffusion models can generate varied images, but their uncontrollable process makes continuous, content-consistent video generation difficult. VideoControlNet combines ControlNet with motion-guided I-, P-, and B-frame generation; experiments report better metrics and faster inference than Text2Video-Zero on DAVIS. The authors note that detailed motion calls for stronger conditions to control output details.

  • Problem

    Uncontrollable diffusion and frame-by-frame generation make it difficult to produce videos with continuous, consistent content.

    Source: §1 · p. 2

  • Method

    VideoControlNet uses ControlNet for the first frame, motion-guided P-frame generation with inpainting, and motion-guided interpolation for the remaining B-frames.

    Source: §1 · p. 2, §3.1 · p. 4

  • Results

    On DAVIS, VideoControlNet outperforms Text2Video-Zero on all reported objective metrics with faster inference, and receives 74.7% user preference against the compared prior methods.

    Source: §4.2 · pp. 6–7

  • Takeaways & Limitations

    The framework translates input videos into different styles or contents while aiming to preserve content consistency through motion information.

    Source: §1 · p. 2

    Detailed motion information requires stronger conditions to control output details, and the method may be enhanced with more effective optical-flow networks.

    Source: §2.3 · p. 4, §4.4 · p. 8

Abstract

from arXiv · show

Recently, diffusion models like StableDiffusion have achieved impressive image generation results. However, the generation process of such diffusion models is uncontrollable, which makes it hard to generate videos with continuous and consistent content. In this work, by using the diffusion model with ControlNet, we proposed a new motion-guided video-to-video translation framework called VideoControlNet to generate various videos based on the given prompts and the condition from the input video. Inspired by the video codecs that use motion information for reducing temporal redundancy, our framework uses motion information to prevent the regeneration of the redundant areas for content consistency. Specifically, we generate the first frame (i.e., the I-frame) by using the diffusion model with ControlNet. Then we generate other key frames (i.e., the P-frame) based on the previous I/P-frame by using our newly proposed motion-guided P-frame generation (MgPG) method, in which the P-frames are generated based on the motion information and the occlusion areas are inpainted by using the diffusion model. Finally, the rest frames (i.e., the B-frame) are generated by using our motion-guided B-frame interpolation (MgBI) module. Our experiments demonstrate that our proposed VideoControlNet inherits the generation capability of the pre-trained large diffusion model and extends the image diffusion model to the video diffusion model by using motion information. More results are provided at our project page.

1 INTRODUCTION

VideoControlNet addresses the difficulty of generating continuous, content-consistent videos with diffusion models by combining ControlNet with motion-guided frame generation. It uses motion to preserve content while supporting outputs in different styles or contents from different prompts.

1 INTRODUCTION

  • Diffusion-based video generation has struggled to produce continuous, content-consistent videos, while frame-by-frame conditioning can still yield inconsistent neighboring frames.The paper attributes this to the uncontrollable diffusion process: one condition can correspond to multiple possible output contents.Source: §1 · p. 2
  • VideoControlNet uses motion information to avoid regenerating redundant areas, with diffusion-model inpainting covering residual areas for content consistency.It generates an I-frame with ControlNet, P-frames with motion-guided P-frame generation (MgPG), and B-frames with motion-guided B-frame interpolation (MgBI).Source: §1 · p. 2
  • VideoControlNet aims to inherit StableDiffusion’s generation capability and translate input videos into diverse videos with different styles or contents.The introduction illustrates this with a woman walking in a forest rendered in soft-painting and cyberpunk styles while retaining the input content.Source: §1 · p. 2

2 RELATED WORKS

The related work covers image diffusion and its controls, diffusion-based video generation and video translation, and optical-flow estimation. VideoControlNet builds on pretrained image diffusion and uses motion information for video consistency.

2.1 Diffusion Model

  • ControlNet adds conditions such as canny, depth, or segmentation maps to pretrained diffusion models, while video diffusion approaches include 3D U-Nets, cascaded generation, and temporal tuning.The reviewed video methods do not use motion information to prevent regeneration of redundant areas, which the authors associate with content inconsistency.Source: §2.1 · p. 3

2.2 Video-to-Video Translation

  • Earlier video-to-video approaches include framewise image translation and GAN-based methods, while Gen-1 uses depth conditioning and retrains diffusion on video data.VideoControlNet instead uses pretrained StableDiffusion and motion information to avoid regenerating redundant areas.Source: §2.2 · p. 3

2.3 Optical Flow Estimation

  • The framework uses FlowFormer for optical flow because the authors describe it as more general across video types than SpyNet, and they identify more effective flow networks as a future improvement.Source: §2.3 · pp. 3–4
  • MgPG combines warp residuals and an occlusion map to form an inpainting mask, then warps the reference frame and inpaints uncertain areas with StableDiffusion and ControlNet.Source: Figure 3 · p. 4, §2.3 · p. 4

3 METHOD

VideoControlNet organizes translation into I-, P-, and B-frames: it generates key frames with diffusion and ControlNet, then uses motion-guided warping and inpainting or interpolation to produce the remaining frames.

3.1 Overview

  • The pipeline generates an I-frame with StableDiffusion and ControlNet, then uses each GoP’s P-frame as the reference for generating later GoPs.The first GoP uses the I-frame as its reference; each GoP ends with a P-frame, and intervening frames are B-frames.Source: §3.1 · p. 4

3.2 Motion-guided P-frame Generation

  • MgPG backward-warps the previous I/P-frame using input-video motion, then inpaints areas selected from residual and occlusion information.Residuals alone may miss occluded regions, so the mask also uses forward-warped inverse flow; masked areas are inpainted with StableDiffusion and ControlNet.Source: §3.2 · pp. 4–5

3.3 Motion-guided B-frame Interpolation

  • MgBI generates B-frames by interpolation without the time-consuming diffusion model.The method uses the reference I/P-frames and motion information extracted from input frames.Source: §3.3 · pp. 5–6
  • MgBI estimates motion from the two nearest I/P-frames, backward-warps both, and combines them using match scores informed by residuals and occlusion.The two references can cover occlusions in one another; the output B-frame is formed from weighted warped frames.Source: §3.3 · pp. 5–6

4 EXPERIMENTS

On DAVIS, VideoControlNet outperforms Text2Video-Zero on all reported metrics and inference speed, and a user study reports 74.7% preference for VideoControlNet. The experiments also examine qualitative results, conditioning choices, inpainting masks, runtime, and application scope.

4.1 Experimental Setup

  • Experiments use StableDiffusion 1.5 with depth- or canny-conditioned ControlNet and evaluate across HEVC, UVG, MCL-JCV, and DAVIS video datasets.The setup also uses DDIM sampling and a pretrained Flowformer for optical-flow estimation.Source: §4.1 · p. 6

4.2 Quantitative Results

  • VideoControlNet achieves 74.7% user preference across 720 votes from 24 users comparing it with Text2Video-Zero and CCPL.The evaluation sampled 100 video-prompt pairs from DAVIS.Source: Table 1 · p. 6, §4.2 · p. 6
  • On DAVIS, VideoControlNet outperforms Text2Video-Zero on FVD, IS, FID, CLIPSIM, LPIPS, and optical-flow error, with faster inference.The comparison uses input-video conditions and reports all six metrics.Source: §4.2 · pp. 6–7, Table 2 · p. 7

4.3 Qualitative Results

  • Against Text2LIVE, VideoControlNet produces more realistic-looking results in examples, while Text2LIVE has good temporal smoothness but variable quality.The paper reports more than 10 hours to edit one video with Text2LIVE, compared with about 3.4 seconds per frame for VideoControlNet.Source: §4.3 · p. 7

4.4 Model Analysis

  • Depth-map conditioning produces more spatial, three-dimensional results, whereas canny-map conditioning retains more detail.The paper illustrates these differences with generated fish and bear fur, respectively.Source: §4.4 · p. 7, Figure 6 · p. 7
  • The P-frame inpainting mask combines occlusion and residual maps because optical flow can be inaccurate for large motion and residual maps are sparse.The paper shows the combined mask covering occlusion areas.Source: §4.4 · pp. 7–8, Figure 7 · p. 8
  • With a GoP size of 10, 20 diffusion steps, and 960×540 input, average generation takes about 3.4 seconds per frame versus 13.7 seconds for framewise generation.The reported evaluation uses a single Tesla V100 GPU.Source: §4.4 · p. 8, Table 3 · p. 8
  • The method supports video style transfer and masked object editing, but its conditions must convey detailed motion, so segmentation maps and human poses are unsuitable.Source: §4.4 · p. 8

5 CONCLUSION

VideoControlNet combines StableDiffusion and ControlNet with motion-guided P-frame generation and B-frame interpolation. The conclusion reports high-quality content consistent with input motion and identifies learnable networks for improved consistency as future work.

5 CONCLUSION

  • VideoControlNet uses MgPG to generate P-frames by inpainting occlusion areas and MgBI to interpolate B-frames from the nearest I/P frames.Source: §5 · p. 8
  • The framework adopts input-video motion information to improve content consistency in video-to-video translation.Source: §5 · p. 8
  • The authors identify adding learnable networks for better content consistency as future work.Source: §5 · p. 8

A VIDEO EXAMPLES

The appendix provides video examples of VideoControlNet outputs. The authors report that the generated videos retain the input motion and content while supporting varied videos and prompts.

A VIDEO EXAMPLES

B ILLUSTRATION OF OCCLUSION MAP GENERATION

The occlusion-map module forward-warps a map of ones with optical flow to locate newly occurring areas, which are combined with residual areas to determine what to inpaint.

B ILLUSTRATION OF OCCLUSION MAP GENERATION

C APPLICATIONS

The supplementary materials illustrate VideoControlNet applications in style transfer and video editing, including an example edit guided by masks and prompts.

C APPLICATIONS

  • VideoControlNet is presented as supporting both style transfer and video editing applications.Source: C APPLICATIONS · p. 10
  • Figure 10 places input videos in the first and fourth rows, edit masks in the second and fifth, and generated outputs in the third and last rows.Source: Figure 10 · p. 11
  • Figure 10's output prompts are “A robot is hiking” and “polar bear,” respectively.Source: Figure 10 · p. 11

C.1 Video Style Transfer

VideoControlNet translates input videos into prompt-controlled styles; the illustrated results include oil painting, cartoon, Chinese painting, and watercolor.

C.1 Video Style Transfer

  • The generated style-transfer results include oil painting, cartoon, Chinese painting, and watercolor styles.Source: §C.1 · p. 11
  • Different prompts control the styles shown in rows beneath the input video.Source: §C.1 · p. 11
  • The authors present these style-transfer results as further evidence of the method's effectiveness.Source: §C.1 · p. 11

C.2 Video Editing

For video editing, VideoControlNet uses supplied masks and prompts to generate edited content; the example replaces a hiking man with a robot.

C.2 Video Editing

  • Given an input video, edit masks, and prompts, VideoControlNet generates output video with content edits based on those masks and prompts.Source: §C.2 · p. 11
  • In the example, masking the hiking man and prompting “a robot is hiking” produces a hiking robot.Source: §C.2 · p. 11
  • The editing masks are generated using the official code of “Segment Anything” [18].Source: §C.2 · p. 11
Loading 2307.14073v2…