Enabling Versatile Controls for Video Diffusion Models (2503.16983v1)

Abstract: Despite substantial progress in text-to-video generation, achieving precise and flexible control over fine-grained spatiotemporal attributes remains a significant unresolved challenge in video generation research. To address these limitations, we introduce VCtrl (also termed PP-VCtrl), a novel framework designed to enable fine-grained control over pre-trained video diffusion models in a unified manner. VCtrl integrates diverse user-specified control signals-such as Canny edges, segmentation masks, and human keypoints-into pretrained video diffusion models via a generalizable conditional module capable of uniformly encoding multiple types of auxiliary signals without modifying the underlying generator. Additionally, we design a unified control signal encoding pipeline and a sparse residual connection mechanism to efficiently incorporate control representations. Comprehensive experiments and human evaluations demonstrate that VCtrl effectively enhances controllability and generation quality. The source code and pre-trained models are publicly available and implemented using the PaddlePaddle framework at http://github.com/PaddlePaddle/PaddleMIX/tree/develop/ppdiffusers/examples/ppvctrl.

A Unified Framework for Fine-Grained Control in Video Diffusion Models

This document provides an overview of VCtrl (also termed PP-VCtrl), a framework designed to add versatile and fine-grained control capabilities to pre-trained text-to-video diffusion models (Zhang et al., 21 Mar 2025 ). It addresses the common challenge where existing models struggle with precise spatiotemporal control, and prior controllable methods are often limited to specific tasks or inefficiently adapt image-based techniques. VCtrl introduces a unified approach using a generalizable conditional module to handle diverse control signals like Canny edges, segmentation masks, and human keypoints without altering the core video generation model.

1. Background and Motivation

Controllable video generation research has historically faced limitations. Task-specific methods, while effective for their designated control type (e.g., edge-to-video), lack the flexibility to adapt to different control signals without significant retraining. Alternatively, adapting controllable image generation models (like ControlNet) for video often introduces temporal inconsistencies or computational inefficiencies, as these models aren't inherently designed for sequential data.

Pre-trained text-to-video diffusion models, such as CogVideoX, have shown remarkable progress in generating videos from text prompts. However, controlling specific aspects like object trajectories, poses, or shapes based on user inputs beyond text remains difficult. VCtrl aims to bridge this gap by providing a unified mechanism that leverages the strengths of large pre-trained video models while layering precise, multi-modal control on top. It achieves this through a modular design that integrates various control signals efficiently.

2. The VCtrl Framework Architecture

VCtrl introduces several key components to enable versatile control over pre-trained video diffusion models without modifying their weights:

1. Unified Control Signal Encoding Pipeline: This pipeline ensures that different types of control signals (e.g., Canny edge maps, segmentation masks, pose keypoint sequences) are processed into a consistent format.
   • Encoding: A sequence of control signals (e.g., video frames of Canny edges), denoted as $v_c$, is first encoded into a latent representation $z_c$ using the encoder $E$ of a pre-trained Variational Autoencoder (VAE).
   • Task-Aware Masking: The latent control representation $z_c$ is concatenated channel-wise with a task-aware mask sequence $M_c$. This mask indicates which temporal or spatial parts of the input are subject to control. The result is a unified control representation $z_m = z_c \oplus M_c$.
2. VCtrl Module: This is a lightweight conditional module, implemented as a Transformer Encoder, that processes the unified control signal $z_m$ alongside features extracted from the main video diffusion network.
   • Architecture: To maintain efficiency, the VCtrl module is significantly smaller than the base diffusion model (e.g., containing 1/5th the number of blocks). Its parameters, $\Theta_c$, are the only ones trained, while the base model remains frozen.
   • DistAlign Layer: A novel "DistAlign" layer is included within the VCtrl module. This layer adaptively scales the control signal features to mitigate potential noise and inconsistencies arising from the varying scales and distributions of different control signal types (e.g., sparse keypoints vs. dense segmentation masks).
3. Sparse Residual Connection Mechanism: This mechanism efficiently injects the control information processed by the VCtrl module back into the frozen base diffusion model.
   • Control Points: Control is applied only at specific blocks within the base network, designated as $N$ control points, typically chosen at regular intervals $I$. This sparse injection strategy minimizes computational overhead and preserves the stability of the pre-trained model.

   • Residual Fusion: At each control point $i$, the output of the VCtrl block ($y_c^i$) is added to the output of the corresponding base model block ($y_b^i$) after dimensionality matching using an AdaptiveAvgPool layer:

     $x_b^{i+1} = y_b^i + \text{AdaptiveAvgPool}(y_c^i)$

     where $x_b^{i+1}$ becomes the input to the next base block. This residual connection allows the control signal to guide the generation process without disrupting the learned representations of the base model.

4. Data Filtering Pipeline: To train the VCtrl modules effectively, a multi-stage data filtering pipeline is used to curate high-quality (video, text, control signal) triplets from raw video datasets.
   • Visual Filter: Removes low-quality content using scene segmentation, border removal, and aesthetic filtering.
   • CLIP Score Filter: Ensures semantic alignment between the video content and its text caption by re-captioning videos and filtering based on CLIP scores.
   • Task-Aware Filter: Extracts specific control signals (e.g., Canny edges, segmentation masks via SAM, human keypoints via ViTPose) and preprocesses them (e.g., temporal smoothing for pose sequences) to create clean training pairs for each control task.

3. Implementation and Evaluation Setup

VCtrl's effectiveness was demonstrated using the CogVideoX-5B text-to-video model and its image-to-video variant (CogVideoX-I2V) as the base diffusion models.

• Training: During training, only the parameters of the VCtrl modules ($\Theta_c$) were updated; the weights of the base CogVideoX model remained frozen. The training used 49-frame video sequences at a resolution of 720x480 or 480x720.
• Evaluation Tasks: The framework was evaluated on three primary control tasks:
  • Canny-to-Video: Generating video based on edge maps.
  • Mask-to-Video: Generating video based on segmentation masks.
  • Pose-to-Video: Generating video based on human pose keypoint sequences.
• Metrics: Performance was measured using both general video quality metrics and task-specific control precision metrics:
  • Video Quality: Fréchet Video Distance (FVD), Subject Consistency, and Aesthetic Score. Lower FVD indicates better quality.
  • Control Precision:
  • Canny Matching: Adaptive Dice coefficient between generated video Canny edges and ground truth edges. Higher is better.
  • Masked Subject Consistency (MS-Consistency): Normalized L1 distance within masked regions between generated and ground-truth videos. Higher is better.
  • Pose Similarity: Object Keypoint Similarity (OKS) between predicted and ground-truth poses. Higher is better.
• Baselines: VCtrl was compared against methods like Text2Video-Zero, Control-A-Video, CoCoCo, Moore-AnimateAnyone, and ControlNeXt-SVD, selected based on task relevance.

4. Experimental Results and Analysis

VCtrl demonstrated state-of-the-art performance across the evaluated tasks, showing significant improvements in both video quality and adherence to control signals.

• Qualitative Results: Generated videos showed high fidelity, strong temporal coherence, and accurate alignment with the input controls (edges, masks, or poses). Compared to baselines like Text2Video-Zero (often low quality) and Control-A-Video (temporal inconsistencies), VCtrl produced visually superior and more consistent results. The VCtrl-I2V variant, leveraging an initial image frame, further improved temporal stability and initial frame fidelity.
• Quantitative Results: VCtrl consistently outperformed baselines on quantitative metrics. For instance, in Canny-to-Video, VCtrl-I2V achieved a much lower FVD (345 vs. 1761.82 for T2V-Zero) and higher Canny Matching (0.28 vs. 0.20). Similar gains were observed for Mask-to-Video and Pose-to-Video tasks using MS-Consistency and Pose Similarity metrics, respectively.

Task	Model	FVD	Canny Matching	MS-Consistency	Pose Similarity
Canny-to-Video	VCtrl-I2V-Canny	345	0.28	N/A	N/A
Canny-to-Video	T2V-Zero	1761.82	0.20	N/A	N/A
Mask-to-Video	VCtrl	*	N/A	Higher	N/A
Mask-to-Video	Baseline(s)	*	N/A	Lower	N/A
Pose-to-Video	VCtrl	*	N/A	N/A	Higher
Pose-to-Video	Baseline(s)	*	N/A	N/A	Lower

Note: Specific quantitative scores vary by baseline and are detailed in the paper (Zhang et al., 21 Mar 2025 ). "Higher" indicates superior performance for VCtrl compared to baselines.

• Ablation Studies: These studies confirmed the effectiveness of key design choices:
  • Connection Layout: The "Space" layout (sparse, evenly distributed injection points) performed best compared to injecting control only at the end ("End") or at every block ("Even").
  • Module Complexity: A "Medium" complexity VCtrl module (1:5 ratio of VCtrl blocks to base blocks) offered the best trade-off between performance and computational efficiency.
• User Study: A blind user paper involving domain experts corroborated the quantitative findings. Participants consistently preferred VCtrl's outputs over baselines regarding overall quality, temporal consistency, and adherence to control conditions across all tasks.

5. Practical Significance and Limitations

VCtrl offers significant practical advantages for developers and researchers working on video generation:

• Unified Control: Provides a single framework to handle multiple control types, simplifying development and enabling combined controls.
• Efficiency: The lightweight VCtrl module and sparse connection strategy minimize computational overhead, making fine-grained control feasible without extensive resources. Training only the small VCtrl modules is much faster than training or fine-tuning the entire base model.
• Preserves Pre-trained Knowledge: By keeping the base diffusion model frozen, VCtrl leverages the powerful generative capabilities learned by large models while adding controllability.
• Modularity and Extensibility: The design allows VCtrl to be potentially adapted to different block-based video generation architectures and extended to new control modalities (e.g., depth maps, audio).

Despite its strengths, VCtrl has potential limitations:

• Data Dependence: The quality of control relies on the quality and quantity of the filtered training data. The data filtering pipeline, while crucial, adds preprocessing overhead.
• Control Signal Quality: Performance may degrade if the input control signals are noisy, ambiguous, or inconsistent over time.

Future work could focus on automating and optimizing the data filtering pipeline, exploring additional control signals, enhancing robustness to noisy inputs, and investigating methods for combining multiple control signals simultaneously.

6. Conclusion

VCtrl presents a significant advancement in controllable video generation by introducing a unified, efficient, and effective framework for integrating diverse control signals into pre-trained video diffusion models (Zhang et al., 21 Mar 2025 ). Its ability to provide fine-grained spatiotemporal control over aspects like edges, masks, and poses, without retraining the base model, addresses key limitations of previous approaches. Demonstrated through strong empirical results and user validation, VCtrl offers a practical solution for creating high-quality, precisely controlled videos, paving the way for more sophisticated applications in animation, content creation, and simulation.