Source-linked AI summary
Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation
Li Hu, Xin Gao, Peng Zhang, Ke Sun, Bang Zhang, Liefeng Bo
TL;DR
Character image-to-video synthesis must preserve detailed appearance consistently while following pose sequences, a challenge for existing character-animation methods. Animate Anyone extends Stable Diffusion with reference-detail, pose-control, and temporal components, achieving state-of-the-art results across character-video benchmarks and broader character types.
Problem
Existing character-animation and image-to-video methods struggle with fine-grained appearance consistency, temporal stability, and precise character details.
Method
Animate Anyone extends Stable Diffusion with ReferenceNet for spatial appearance details, a lightweight Pose Guider, and temporal layers for continuous motion.
Results
Animate Anyone achieves state-of-the-art results on fashion, dance, and talk-video benchmarks and outperforms general image-to-video approaches in character animation.
Takeaways & Limitations
The framework can animate arbitrary characters while maintaining high-definition spatial and temporal appearance consistency.
Takeaways & Limitations
The model may produce hand-motion distortions or blur, struggles with unseen character parts, and has lower operational efficiency than non-diffusion methods.
Abstract
from arXiv · showhide
Character Animation aims to generating character videos from still images through driving signals. Currently, diffusion models have become the mainstream in visual generation research, owing to their robust generative capabilities. However, challenges persist in the realm of image-to-video, especially in character animation, where temporally maintaining consistency with detailed information from character remains a formidable problem. In this paper, we leverage the power of diffusion models and propose a novel framework tailored for character animation. To preserve consistency of intricate appearance features from reference image, we design ReferenceNet to merge detail features via spatial attention. To ensure controllability and continuity, we introduce an efficient pose guider to direct character's movements and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames. By expanding the training data, our approach can animate arbitrary characters, yielding superior results in character animation compared to other image-to-video methods. Furthermore, we evaluate our method on benchmarks for fashion video and human dance synthesis, achieving state-of-the-art results.
1. Introduction
Character animation converts source character images into realistic videos guided by pose sequences, but existing methods suffer from distorted details, semantic inconsistency, and temporal instability. Animate Anyone addresses these challenges with reference-detail modeling, pose control, and temporal modeling, achieving strong character-animation results across benchmarks.
- Character animation generates realistic videos from source character images according to desired posture sequences for applications including retail, entertainment, art, and virtual characters.
- Existing image-animation and pose-transfer methods can produce local distortion, blurred details, semantic inconsistency, and temporal instability.
- Image-to-video extensions provide diversity but often fail to capture intricate image details, causing temporal variation in fine-grained character appearance.
- Animate Anyone uses ReferenceNet with spatial attention, a lightweight pose guider, and temporal layers to preserve appearance, control motion, and smooth transitions.
- The model is trained on 5K character video clips and achieves state-of-the-art results on fashion, dance, and talk-video benchmarks while animating characters across domains.
2. Related Works
Related work develops diffusion-based image and video generation by transferring text-to-image architectures, adding temporal modeling, and incorporating image conditions. These approaches improve generation quality or controllability but remain limited in stable, detailed character animation.
- Diffusion-based text-to-image methods became mainstream because of their strong generation quality, while latent diffusion reduces computational complexity.
- Text-to-video methods adapt text-to-image models with inter-frame attention or temporal layers to generate videos from pretrained image-generation capabilities.
- Image-to-video methods incorporate image latents, conditional image inputs, or CLIP visual features into diffusion-based generation.
- Existing image-to-video approaches still struggle with stable human-video generation and precise preservation of fine-grained character details.
- Diffusion-based image-animation methods explore texture injection, optical-flow conditioning, motion codes, image embeddings, and decoupled pose-background control.
3. Methods
The method extends Stable Diffusion for pose-guided character video generation by combining reference-image features, pose conditioning, and temporal modeling. A two-stage training process separates frame-level learning from multi-frame temporal learning.
- Overview: Given a reference character image and pose sequence, the model generates an animated video using a Stable Diffusion-based denoising pipeline.
- Stable Diffusion: Stable Diffusion encodes images into latent representations, trains a denoising UNet to predict applied noise, and decodes restored latents into generated images.
- ReferenceNet: ReferenceNet extracts reference-image appearance features and integrates them into corresponding denoising-UNet layers through spatial attention.
- ReferenceNet: CLIP features alone are insufficient for detail consistency because their 224×224 inputs and semantic training emphasize high-level information over fine-grained details.
- Pose Guider and Temporal Layer: A lightweight Pose Guider aligns pose images with the noise-latent resolution and adds the processed pose signal before denoising, while temporal layers model inter-frame dependencies.
- Training: Training first uses individual frames without temporal layers, then trains only the temporal layer on 24-frame clips while fixing the remaining network weights.
4. Experiments
Experiments show that Animate Anyone can animate diverse character images while preserving appearance and temporal continuity, and it performs strongly across fashion, dance, and talking-gesture benchmarks. Ablations further support ReferenceNet and temporal modeling as important design components.
- Qualitative Results: Animate Anyone generates high-definition videos for full-body, half-length, cartoon, and humanoid characters while maintaining appearance consistency and temporal continuity.The method remains consistent with reference images even under substantial motion and produces continuous inter-frame movement.
- Fashion Video Synthesis: On fashion videos, Animate Anyone preserves fine-grained clothing details more consistently than other methods, including color and fine structural elements.The comparison emphasizes clothing-detail consistency, where competing methods show noticeable errors.
- Human Dance Generation: Animate Anyone achieves the best results on the TikTok human-dance benchmark and maintains visual continuity during intricate dance sequences.The method also shows robustness across diverse character appearances, despite DisCo using human-attribute pre-training.
- Talking Gesture Generation: On the Ted-talk benchmark, Animate Anyone significantly outperforms DisCo and SD-I2V, achieving better results using only pose information than methods using ground-truth images as driving signals.MRAA and TPSMM use ground-truth images for video reconstruction, whereas Animate Anyone uses pose information.
- General Image-to-Video Methods: General image-to-video methods struggle with substantial character movements and long-term appearance consistency, limiting their support for consistent character animation.AnimateDiff and Gen-2 are compared only on appearance fidelity because they do not provide pose control.
- Ablation Studies: Ablations indicate that ReferenceNet preserves character-appearance details, while removing temporal layers causes texture sticking, inter-frame jitter, and a significant FVD decline.The temporal-modeling study also reports image-quality degradation when two-stage training is omitted.
5. Discussion and Conclusion
Animate Anyone preserves character appearance while providing pose-controlled, temporally continuous animation, but remains limited by hand-motion distortions, unseen-part instability, and lower diffusion-model efficiency.
- ReferenceNet preserves intricate character appearances while the framework supports efficient pose controllability and temporal continuity.
- The method applies to general character animation and outperforms existing methods.
- Hand movements may produce distortions and motion blur, making stable hand animation a limitation.
- Animating unseen character parts from a single image perspective is ill-posed and can be unstable during movement.
- Using DDPM lowers operational efficiency compared with methods that do not rely on diffusion models.