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Control-A-Video: Controllable Text-to-Video Diffusion Models with Motion Prior and Reward Feedback Learning

Weifeng Chen, Yatai Ji, Jie Wu, Hefeng Wu, Pan Xie, Jiashi Li, Xin Xia, Xuefeng Xiao, Liang Lin

arXiv:2305.13840v3cs.CVcs.AIcs.LGcs.MM

TL;DR

Text-to-video diffusion methods still struggle to produce videos with high quality and motion consistency. Control-A-Video combines text and control-map conditioning with content and motion priors and reward feedback. The authors report higher-quality, more consistent videos and state-of-the-art results for controllable text-to-video generation.

  • Problem

    Text-to-video methods often struggle to produce videos with high visual quality and motion consistency, including object consistency and smooth, non-flickering motion.

    Source: §1 · p. 2

  • Method

    Control-A-Video conditions video diffusion on text and control maps, using first-frame content conditioning, motion-aware noise initialization, and reward feedback learning.

    Source: §1 · pp. 2–3, §5 · p. 14

  • Results

    The authors report state-of-the-art controllable text-to-video results, with higher-quality and more consistent videos than existing methods.

    Source: §1 · p. 4, §5 · p. 14

  • Takeaways & Limitations

    The authors conclude that prior initialization and reward feedback offer avenues for improving the quality and controllability of diffusion-based video synthesis.

    Source: §5 · pp. 14–15

Abstract

from arXiv · show

Recent advances in text-to-image (T2I) diffusion models have enabled impressive image generation capabilities guided by text prompts. However, extending these techniques to video generation remains challenging, with existing text-to-video (T2V) methods often struggling to produce high-quality and motion-consistent videos. In this work, we introduce Control-A-Video, a controllable T2V diffusion model that can generate videos conditioned on text prompts and reference control maps like edge and depth maps. To tackle video quality and motion consistency issues, we propose novel strategies to incorporate content prior and motion prior into the diffusion-based generation process. Specifically, we employ a first-frame condition scheme to transfer video generation from the image domain. Additionally, we introduce residual-based and optical flow-based noise initialization to infuse motion priors from reference videos, promoting relevance among frame latents for reduced flickering. Furthermore, we present a Spatio-Temporal Reward Feedback Learning (ST-ReFL) algorithm that optimizes the video diffusion model using multiple reward models for video quality and motion consistency, leading to superior outputs. Comprehensive experiments demonstrate that our framework generates higher-quality, more consistent videos compared to existing state-of-the-art methods in controllable text-to-video generation

1 Introduction

Text-to-video diffusion still struggles to produce videos with both high visual quality and motion consistency. Control-A-Video combines text and control-map conditioning with content and motion priors and reward feedback to address these challenges.

1 Introduction

  • Existing text-to-video approaches often struggle with visual quality and motion consistency, including object consistency and smooth, non-flickering motion.The paper distinguishes technical quality, such as fewer artifacts and less blur, from subjective aesthetic quality.Source: §1 · p. 2
  • Control-A-Video conditions generation on text and reference control maps, including edge and depth maps.The model reorganizes a pretrained controllable text-to-image model with trainable temporal layers and spatial-temporal self-attention.Source: §1 · p. 2, Figure 1 · p. 2
  • First-frame conditioning supplies a content prior so the model generates subsequent frames, and inference can reuse the last frame to extend videos autoregressively.The authors describe this scheme as separating content from temporal modeling and transferring generative capability from the image domain.Source: §1 · p. 3
  • Residual-based and optical-flow-based noise initialization provide motion priors from reference videos, increasing relevance among frame latents and reducing flicker.The paper also says these strategies help generated videos resemble motion changes in the reference and maintain object consistency.Source: §1 · p. 3
  • ST-ReFL uses multiple reward models to optimize video quality and motion consistency, with experiments reporting fewer artifacts, improved aesthetic appeal, and heightened consistency.The contribution summary characterizes it as feedback learning that scores model outputs.Source: §1 · pp. 3–4
  • The framework reports state-of-the-art results in controllable text-to-video generation, with higher-quality and more consistent videos.The authors attribute improvements in artifacts, aesthetic appeal, and consistency to ST-ReFL training.Source: §1 · pp. 3–4

2 Related Work

The related work traces how diffusion-based image generation supports controllable generation, then reviews video-generation approaches and their consistency trade-offs. Control-A-Video extends this line with a two-stage generation approach, noise manipulation, and reward feedback.

2.1 Text-to-image generation with diffusion models

  • Text-to-image diffusion methods generate text-conditioned images, while ControlNet [54], T2I-Adapter [26], and Composer incorporate control hints such as edges, poses, and depth maps.Reward-feedback methods including ImageReward, RAFT, and DPOK have also been used to optimize diffusion models.Source: §2.1 · p. 4

2.2 Text-to-video generation with diffusion models

  • Video diffusion models extend text-to-image systems, while autoregressive methods such as LVDM, MCVD, and Align-Your-Latents generate longer videos sequentially.The passage identifies VDM [17], Imagen Video [14], Make-A-Video [38], and AnimateDiff as examples trained on large text-video datasets.Source: §2.2 · p. 4
  • Text-based video translation methods use temporal attention, flow constraints, or inference-time tuning, while conditional video models such as Gen-1 [5] and VideoCompose use control maps.The passage notes that zero-shot methods may lack temporal consistency and tuning-based methods require additional tuning.Source: §2.2 · pp. 4–5
  • Control-A-Video contrasts with these approaches through a T2I-I2V generation pipeline, noise manipulation for temporal consistency, and ST-ReFL for video quality and consistency.Its pipeline applies motion-aware noise prediction followed by reward-feedback training.Source: §2.2 · p. 5, Figure 2 · p. 5

3 Method

The method extends a controllable image diffusion model with temporal modeling, conditions video generation on the first frame, and adds motion-adaptive noise priors and reward feedback learning. These components target frame consistency and video quality while supporting longer autoregressive generation.

3.1 Video Diffusion Model with Control

  • Spatial-temporal self-attention combines frame features with keys and values from all frames, giving each position global video context for more consistent generation.Source: §3.1 · p. 6
  • Training conditions denoising on the first frame, prompt, and control maps; inference can reuse generated frames as initial frames to produce longer videos autoregressively.Source: §3.1 · pp. 6–7

3.2 Motion-adaptive Noise Prior

  • Motion-adaptive noise preserves latent-frame similarity better than per-frame Gaussian noise, with flow-based noise closest to the original video distribution and residual noise also highly correlated.Source: §3.2 · pp. 7–8, Figure 3 · p. 8
  • Residual-based noise shares noise across unchanged regions while varying it in moving regions; flow-based noise propagates patterns along optical flow between frames.Source: §3.2 · p. 8

3.3 Reward Feedback Learning for Controllable Video Generation

  • ST-ReFL combines residual- and flow-based motion rewards with technical- and aesthetic-quality rewards, then uses their losses to update the video diffusion model.Source: §3.3 · p. 9

4 Experiments

Control-A-Video is evaluated on video-generation setup, qualitative and quantitative comparisons, and ablations of its motion priors and reward feedback. The reported results favor the model overall, while the ablations examine how each component relates to video quality and consistency.

4.1 Implementation Details

  • Training used 0.1M film clips and 0.1M LAION image-text pairs; temporal layers were trained on mixed video and image data at 512×512 for 20k steps.Source: §4.1 · p. 10

4.2 Main Results

  • Depth maps yielded more diverse outputs, while edge maps retained finer details, including clothing patterns in a human-to-robot transformation.Source: Controllable Video Generation · pp. 10–11
  • In editing comparisons, Control-A-Video was described as clearest, most consistent, and most text-aligned against AnimateDiff, Text2Video-Zero, and VideoComposer.The comparison includes a fast-moving dog and a slow-moving camel.Source: Qualitative Analysis · pp. 11–12, Figure 4 · p. 11
  • Four of six metrics favored Control-A-Video; it led prompt CLIP and depth errors, with +2.3 MUSIQ and +0.36 ImageReward over previous bests.Text2Video-Zero had a higher frames-CLIP score, which the authors attribute as likely due to its smaller movement range; the aesthetic-predictor score was comparable to AnimateDiff.Source: Table 1 · p. 11, Quantitative Comparison · pp. 12–13
  • In a 20-participant user study rating textual alignment, consistency, and visual quality from 1 to 5, Control-A-Video achieved the best human-evaluation performance.Source: Quantitative Comparison · p. 13

4.3 Ablation Studies

  • Both residual-based and optical-flow-based noise initialization improved all metrics except MUSIQ over the depth-control baseline, with ImageReward gains of 0.144 and 0.194, respectively.The qualitative comparison also reports flickering and artifacts without a motion prior, versus consistency with optical-flow initialization and stable backgrounds with residual initialization.Source: Table 2 · p. 12, Figure 5 · p. 12, §4.3 · p. 13
  • The complete reward-feedback model achieved the best overall balance across metrics; removing aesthetic, technical, residual, or optical-flow rewards worsened specific reported outcomes.Without technical reward, MUSIQ was worst; removing residual reward worsened depth error and frame quality, while optical-flow reward improved motion consistency and ImageReward.Source: Spatial-Temporal Reward Feedback… · pp. 13–14, Table 3 · p. 14
  • Before-and-after examples show ST-ReFL feedback tuning from motion and quality reward models improving video consistency and quality.Source: Spatial-Temporal Reward Feedback… · p. 13, Figure 6 · p. 14

5 Conclusion

Control-A-Video generates videos from text prompts and reference control maps by combining content and motion priors with reward feedback learning. The framework targets improved video quality and temporal consistency.

5 Conclusion

  • The framework conditions video generation on text prompts and control maps while combining prior initialization with reward feedback learning.The conclusion identifies this combination as an approach to improving quality and controllability in diffusion-based video synthesis.Source: §5 · pp. 14–15
  • A first-frame content prior transfers image-domain information to video generation, while residual- and optical-flow-based noise initialization supplies motion priors.The conclusion associates these strategies with video generation from an initial frame and improved temporal consistency.Source: §5 · p. 14
  • ST-ReFL optimizes the video diffusion model with dedicated reward models, improving video quality and motion coherence.The conclusion describes reward feedback learning as one of the framework's combined strategies.Source: §5 · pp. 14–15

A Preliminary

This section introduces diffusion as a process of adding noise and learning to reverse it, then describes latent-space and control-conditioned variants. For video, the control input extends across a sequence of frames.

A Preliminary

  • The forward process gradually adds variance-β_t noise until the signal reaches x_T ∈ N(0, I), and the reverse process denoises toward the original x_0.The section presents the forward and reverse transitions as the diffusion model's basic processes.Source: A Preliminary · p. 16
  • With fixed reverse-process variance, the model predicts the mean through a denoiser trained to predict added noise, conditioned on the text prompt.The noise-prediction loss is expressed with the text condition c_p.Source: A Preliminary · p. 16
  • Latent diffusion encodes an image into a compressed latent code for denoising, then decodes the reconstructed latent to obtain the image and speed denoising.The encoder is E and the decoder is D.Source: A Preliminary · p. 16
  • ControlNet adds trainable layers copied from a pretrained image diffusion model and fine-tunes them on control maps such as edges, depth, or segmentation.For video, the input signal and control map extend to sequences of N frames.Source: A Preliminary · p. 16

B.1 Cases show

The cases illustrate ablations of motion prior and ST-ReFL, residual-noise threshold choices, control-map options, and autoregressive video extension. They also include a comparison with other methods and supplementary video examples.

B.1 Cases show

  • The supplementary video attachment contains most videos referenced in the paper along with additional cases.More ablation and comparison videos are noted as supplementary materials.Source: §B.1 · p. 17
  • Without motion prior, the camel example flickers and has a distorted background; ST-ReFL improves clarity and visual appeal in the dog example.The ablation passage also reports improved consistency and quality with ST-ReFL.Source: §B.1 · p. 17
  • A residual-noise threshold of 0.1 is reported to balance consistency and smoothness, compared with identical noise at 1.0 and distinct noise at 0.0.The passage presents these as different noise settings after training with residual-based noise.Source: §B.1 · p. 17
  • The cases include a comprehensive comparison of the proposed method with existing video-editing approaches in Figure 4.The passage describes the comparison as intended to highlight the model's advantages.Source: §B.1 · p. 17
  • The frame-conditioning strategy supports autoregressive video editing by repeatedly extending a sequence two or three times.The passage presents this capability as a byproduct of the approach.Source: §B.1 · p. 17
  • Depth control is described as offering greater creative flexibility, while edge control provides greater consistency in edited videos.The passage gives additional cases demonstrating support for different control maps.Source: §B.1 · p. 17

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