Animate Anyone 2: High-Fidelity Character Image Animation with Environment Affordance
On the map: Diffusion models → Video generation
Abstract (arXiv)
Recent character image animation methods based on diffusion models, such as Animate Anyone, have made significant progress in generating consistent and generalizable character animations. However, these approaches fail to produce reasonable associations between characters and their environments. To address this limitation, we introduce Animate Anyone 2, aiming to animate characters with environment affordance. Beyond extracting motion signals from source video, we additionally capture environmental representations as conditional inputs. The environment is formulated as the region with the exclusion of characters and our model generates characters to populate these regions while maintaining coherence with the environmental context. We propose a shape-agnostic mask strategy that more effectively characterizes the relationship between character and environment. Furthermore, to enhance the fidelity of object interactions, we leverage an object guider to extract features of interacting objects and employ spatial blending for feature injection. We also introduce a pose modulation strategy that enables the model to handle more diverse motion patterns. Experimental results demonstrate the superior performance of the proposed method.
Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). Placed in Video generation, one of 14 directions in Diffusion models. Builds on 49 of its 61 references that are in the map. First posted on arXiv 10 Feb 2025.
Builds on
49 of 61 references.
Closest ancestors · 9
Same direction, newest first
- 2024TPC: Test-time Procrustes Calibration for Diffusion-based Human Image Animation31 Oct
- 2024TCAN: Animating Human Images with Temporally Consistent Pose Guidance using Diffusion Models12 Jul
- 2024UniAnimate: Taming Unified Video Diffusion Models for Consistent Human Image Animation3 Jun
- 2024Champ: Controllable and Consistent Human Image Animation with 3D Parametric Guidance21 Mar
- 2023Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation28 Nov
- 2023MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model27 Nov
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasets25 Nov
- 2023AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning10 Jul
- 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models18 Apr
Other references · 40
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- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2020Denoising Diffusion Implicit Modelscited by 9,655
- 2023Segment Anythingcited by 8,158
- 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
- 2017Neural Discrete Representation Learningcited by 4,650
- 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
- 2024SAM 2: Segment Anything in Images and Videoscited by 2,784
- 2018Towards Accurate Generative Models of Video: A New Metric & Challengescited by 1,204
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- 2018Everybody Dance Nowcited by 313
- 2021Motion Representations for Articulated Animationcited by 253
- 2024EMO: Emote Portrait Alive -- Generating Expressive Portrait Videos with Audio2Video Diffusion Model under Weak Conditionscited by 233
- 2024Sapiens: Foundation for Human Vision Modelscited by 229
- 2022Thin-Plate Spline Motion Model for Image Animationcited by 209
- 2018Animating Arbitrary Objects via Deep Motion Transfercited by 206
- 2024MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidancecited by 196
- 2023ProPainter: Improving Propagation and Transformer for Video Inpaintingcited by 193
- 2020Searching Central Difference Convolutional Networks for Face Anti-Spoofingcited by 173
- 2023MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusioncited by 115
- 2019DwNet: Dense warp-based network for pose-guided human video generationcited by 114
- 2024DeMamba: AI-Generated Video Detection on Million-Scale GenVideo Benchmarkcited by 85
- 2015face anti-spoofing based on color texture analysiscited by 82
- 2020Deep Spatial Gradient and Temporal Depth Learning for Face Anti-spoofingcited by 70
- 2022Exploring Dual-task Correlation for Pose Guided Person Image Generationcited by 68
- 2024MIMO: Controllable Character Video Synthesis with Spatial Decomposed Modelingcited by 63
- 2023Putting People in Their Place: Affordance-Aware Human Insertion into Scenescited by 53
- 2020Deep Spatial Transformation for Pose-Guided Person Image Generation and Animationcited by 43
- 2023Bidirectionally Deformable Motion Modulation For Video-based Human Pose Transfercited by 25
- 2024AnchorCrafter: Animate Cyber-Anchors Selling Your Products via Human-Object Interacting Video Generationcited by 25
- 2024MovieCharacter: A Tuning-Free Framework for Controllable Character Video Synthesiscited by 12
- 2024Text2Place: Affordance-aware Text Guided Human Placementcited by 11
- 2024InVi: Object Insertion In Videos Using Off-the-Shelf Diffusion Modelscited by 10
- 2024Dormant: Defending against Pose-driven Human Image Animationcited by 9
- 2023Synthesizing Environment-Specific People in Photographscited by 4
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Citing papers per year: 2025: 36; 2026: 24.
60 papers.
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