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Motion-Conditioned Diffusion Model for Controllable Video Synthesis
Tsai-Shien Chen, Chieh Hubert Lin, Hung-Yu Tseng, Tsung-Yi Lin, Ming-Hsuan Yang
TL;DR
Fine-grained video control over content and motion remains limited in prior diffusion work. MCDiff takes a start frame and motion strokes, completing sparse motion into dense flows before generating future frames. The authors report state-of-the-art visual quality for stroke-guided synthesis and diverse-context and motion capability on MPII Human Pose.
Problem
Prior diffusion-based video synthesis offers limited control over desired content and motion, with existing approaches centered on coarse classes or text prompts.
Source: §1 · p. 2Method
MCDiff uses a start frame and motion strokes, completing sparse flows into dense motion before conditionally generating future frames.
Source: §1 · p. 2Results
MCDiff achieves state-of-the-art visual quality over prior stroke-guided methods on standard benchmarks and demonstrates synthesis across diverse contexts and motions on MPII Human Pose.
Source: §1 · p. 2Takeaways & Limitations
MCDiff provides fine-grained control through strokes, with foreground strokes indicating object movement and background strokes representing camera adjustments.
Source: §1 · p. 2, §5 · p. 8Because flow completion is data-driven, edits well beyond the training distributions are difficult to fulfill.
Source: §5 · p. 8
Abstract
from arXiv · showhide
Recent advancements in diffusion models have greatly improved the quality and diversity of synthesized content. To harness the expressive power of diffusion models, researchers have explored various controllable mechanisms that allow users to intuitively guide the content synthesis process. Although the latest efforts have primarily focused on video synthesis, there has been a lack of effective methods for controlling and describing desired content and motion. In response to this gap, we introduce MCDiff, a conditional diffusion model that generates a video from a starting image frame and a set of strokes, which allow users to specify the intended content and dynamics for synthesis. To tackle the ambiguity of sparse motion inputs and achieve better synthesis quality, MCDiff first utilizes a flow completion model to predict the dense video motion based on the semantic understanding of the video frame and the sparse motion control. Then, the diffusion model synthesizes high-quality future frames to form the output video. We qualitatively and quantitatively show that MCDiff achieves the state-the-of-art visual quality in stroke-guided controllable video synthesis. Additional experiments on MPII Human Pose further exhibit the capability of our model on diverse content and motion synthesis.
1. Introduction
The paper targets finer-grained video control over both subjects and motion, beyond coarse classes or text prompts. MCDiff uses a start image and motion strokes, then completes sparse flows before predicting future frames.
1. Introduction
- The interface uses a reference image as the start frame for content and strokes to specify subject motion and camera adjustments.The stated goal is a sequence that reflects both specified content and motion.Source: §1 · p. 2
- Earlier methods explored similar control on simple datasets, but their synthesis quality remained preliminary.A single-stage conditional diffusion approach also produced dissatisfactory results, which the authors attribute to ambiguous strokes and the need for semantic understanding.Source: §1 · p. 2
- MCDiff divides synthesis into sparse-to-dense flow completion and future-frame prediction to address ambiguity in sparse motion inputs.The flow model uses the frame’s semantic content alongside strokes; the subsequent model generates frames through conditional diffusion.Source: §1 · p. 2
- Experiments on standard benchmarks show state-of-the-art visual quality over prior stroke-guided methods, while MPII Human Pose tests synthesis across diverse contexts and motions.The authors also report that foreground strokes indicate object movements and background strokes represent camera adjustments.Source: §1 · p. 2
2. Related Works
Prior video-control approaches span text prompts, demonstrations, and strokes, but the cited work identifies limits in fine-grained motion control and visual quality. MCDiff is presented as another fine-grained control mechanism for video diffusion.
Video Diffusion Models
- Most video diffusion work cited focuses on unconditional synthesis or coarse-grained text control, while some fine-grained methods require an input video to provide initial motion.Other controls include art-style editing, text-based motion modification, subject changes, and motion extrapolation.Source: §2 · pp. 2–3
Controllable Diffusion Models for Content Synthesis
- MCDiff is framed as finer-grained control for video diffusion, extending image-conditioned future-frame prediction with additional motion guidance.Earlier future-frame prediction methods cited lack extra input conditions for guiding target-video motion.Source: §2 · pp. 2–3
Controllable Video Synthesis
- Motion transfer and image reenactment use motion from a reference video, while playable video synthesis offers learned but coarse-grained actions for a shared subject.The former approaches require a video demonstration; playable synthesis controls spatial location interactively.Source: §2 · p. 3
- Prior stroke-based methods either warp features using predicted dense optical flow or manipulate dense latent variables recurrently; the paper describes their outputs as prone to unnatural distortions.The cited methods include Hao et al., C2M [2], II2V [7], and iPOKE [6].Source: §2 · p. 3
3. Method
MCDiff autoregressively predicts each future frame from the current frame and stroke-specified motion. It completes sparse flows into dense motion maps, then conditions diffusion-based frame synthesis on those maps.
3. Method
- The method takes a start image and sparse stroke flows, and autoregressively predicts future frames using flow completion followed by conditional diffusion.The completion model uses the current frame and momentary sparse flow to predict dense flow; the frame predictor conditions on that frame and flow.Source: §3 · p. 3
3.1. Annotations of Video Dynamics
- Dense-flow annotations combine trajectories from general tracking points and human keypoints, with keypoint trajectories overriding general points where the human-shape prior is more accurate.The annotated trajectories yield dense flow maps between arbitrary frame pairs.Source: §3.1 · p. 4, Figure 3 · p. 4
3.2. Flow Completion Model
- The flow completion model receives the current frame and a sparse-flow map with missing values filled by a learnable embedding, and is trained self-supervised on simulated sparse flows.Keypoint flows are prioritized and sampled by magnitude, while general tracking points represent other objects and background camera movement; a magnitude-based per-pixel weight addresses zero-dominated flow distributions.Source: §3.2 · p. 4
3.3. Future-Frame Prediction
- The future-frame module samples Gaussian noise and iteratively denoises it with a UNet conditioned on the current frame and extracted dense flow.Training uses extracted dense flows and an epsilon-prediction objective.Source: §3.3 · pp. 4–5
3.4. End-to-End Fine-Tuning
- After separate training, the flow completion and frame prediction models are fine-tuned end-to-end to reduce their domain gap and improve their interaction.The combined pipeline conditions frame prediction on the dense flow predicted from the current frame and sparse input strokes.Source: §3.4 · p. 5
4. Experiments
Experiments evaluate MCDiff on two standard benchmarks and MPII Human Pose, comparing it with prior methods and testing its two-stage design. Results report state-of-the-art synthesis quality, an advantage over a single-stage counterpart, and synthesis across diverse content and motion conditions.
4.1. Datasets
- Evaluation uses TaiChi-HD, Human3.6M, and MPII Human Pose, with MPII providing varied activities, multiple people, and camera adjustments.The reported splits include 252/28 TaiChi-HD training/testing videos, 600/239 Human3.6M videos, and 19,609/2,000 MPII clips.Source: §4.1 · p. 5
4.2. Implementation Details
- Training uses latent diffusion modules for flow completion and future-frame prediction, followed by end-to-end fine-tuning of the full pipeline.Frames are encoded from 256×256 to 64×64 latent variables; the pipeline is trained for 400k separate-module iterations and 100k joint fine-tuning iterations.Source: §4.2 · p. 5
4.3. Comparisons with Prior Methods
- MCDiff achieves state-of-the-art synthesis quality on two benchmarks and maintains performance as input stroke counts vary, unlike prior approaches that usually lose quality with more strokes.Qualitative comparisons with iPOKE show better visual quality and closer adherence to stroke-specified motions.Source: Table 1 · p. 6, Figure 4 · p. 6, §4.3 · p. 7
4.4. Failure of Single-Stage Framework
- The two-stage model outperforms its single-stage counterpart on MPII, supporting flow completion before future-frame synthesis for sparse motion inputs.The ablation directly feeds sparse flow maps to the future-frame predictor when flow completion is removed.Source: §4.4 · p. 7, Table 2 · p. 7
4.5. Synthesis with Diverse Contents and Motions
- On MPII, flow completion predicts dense flows for large motions, object interactions, multiple moving objects, and camera viewpoint shifts.Examples include coherent predicted motion for hands interacting with clothes and background motion consistent with viewpoint-control strokes.Source: §4.5 · p. 7, Figure 5 · p. 7
- MCDiff synthesizes high-quality, temporally consistent MPII videos across varied activities and content, including foreground movement and camera viewpoint changes.The experiments use testing-set start frames and manually specified strokes; background strokes indicate camera adjustment.Source: §4.5 · p. 8, Figure 6 · p. 8
5. Conclusion and Limitations
MCDiff provides fine-grained control and shows promising results across three large-scale video datasets. Its scope is limited by training-distribution shifts and weaknesses in the pattern-based motion flows used for training.
Conclusion
- MCDiff shows promising results on three large-scale video synthesis datasets spanning a wide range of human activities.The framework is presented as offering high-quality, high-fidelity synthesis with an intuitive, fine-grained control interface.Source: §5 · p. 8
Limitations
- Edits beyond the training distribution are difficult because the flow completion model is learned in a data-driven manner.The authors identify handling novel object compositions as a challenging future direction.Source: §5 · p. 8
- Pattern-recognition methods used to extract training flows typically fail on textureless surfaces and visual illusions, limitations the model inherits.The authors suggest physically grounded motion-field datasets using sensors or simulations as a way to address these difficult, rare cases.Source: §5 · pp. 8–9
Appendix
The appendix reports quantitative motion-controllability comparisons and additional MPII Human Pose visualizations.
Appendix
- The appendix contains a quantitative comparison of motion controllability and additional visualizations on MPII Human Pose.The comparison is in Section A; the visualizations are in Section B.Source: Appendix · p. 12
A. Comparison of Motion Controllability
The appendix compares MCDiff with II2V and iPOKE on motion controllability using shared start frames and strokes from TaiChi-HD and Human3.6M. MCDiff generally achieves lower ADE and higher recall, with a larger performance gap as the number of input strokes increases.
A. Comparison of Motion Controllability
- The comparison uses the same start frames and strokes for MCDiff, II2V, and iPOKE, sampled from TaiChi-HD and Human3.6M.Tests use 1, 5, or 9 strokes, with the selected human keypoints varying by stroke count.Source: A. Comparison of Motion Controllability · p. 12
- Motion controllability is measured using ADE and recall: ADE averages controlled-keypoint displacement errors, while recall reports the share of detected keypoints.HRNet estimates keypoints in the ending frame; keypoints with confidence below 0.2 count as missed.Source: A. Comparison of Motion Controllability · p. 12
- MCDiff achieves lower ADE and higher recall than prior methods, with a substantial gap across both benchmarks and different stroke counts.The gap grows more significant as the number of input strokes increases.Source: A. Comparison of Motion Controllability · p. 12, Table 3 · p. 12
B. Additional Visualizations on MPII Human Pose
The MPII Human Pose visualizations show the input frame and strokes alongside intermediate motion representations and synthesized frames.
B. Additional Visualizations on MPII Human Pose
- The visualizations place the start frame and strokes on the left, with momentary strokes, dense flow maps, and synthesized frames on the right.The examples use MPII Human Pose test frames and human-specified strokes.Source: B. Additional Visualizations on MPII… · p. 12