arXiv AI
Jun 8
FreeAnimate: Training-Free Human Image Animation with Preview-Guided Denoising
arXiv:2606. 06885v1 Announce Type: cross Abstract: Human Image Animation has seen significant advancements, primarily driven by diffusion models.
MultiAnimate: A Unified Framework for Controllable Multi-Character Animation
Recent advances in generative models and technological innovations have significantly addressed the fundamental challenges of character image animation. However, existing approaches predominantly focus on character animation from a single reference image, substantially limiting their applicability in scenarios such as multiple character interaction animation.
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arXiv AI
Jun 8
Beyond Skeletons: Learning Animation Directly from Driving Videos with Same2X Training Strategy
arXiv:2606. 06903v1 Announce Type: cross Abstract: Human image animation aims to generate a video from a static reference image, guided by pose information extracted from a driving video.
arXiv AI
4d ago
WeLike2Party! In-Context Motion Transfer for Multi-Human Image Animation
arXiv:2609.36937v1 Announce Type: cross Abstract: Human image animation aims to transfer motion from a driving video to subjects in a reference image. Despite remarkable progress in video generation,...
arXiv Computer Vision
Sep 22
Slot-ID: Identity-Preserving Video Generation from Reference Videos via Slot-Based Temporal Identity Encoding
arXiv:2601.01352v2 Announce Type: replace Abstract: Human identity-preserving text-to-video generation remains challenging under large changes in viewpoint, facial expression, illumination, and motio...
Hugging Face Trending Papers
Aug 20
AvatarDynamizer: From Static to Dynamic Human Avatars via Generative Dynamic Textures
For full-body avatars, modeling surface dynamics is crucial for overcoming the uncanny valley and achieving perceptual realism. Person-agnostic methods recover static 3D avatars from monocular images, videos, or text prompts, but their skeleton-driven animations lack realistic surface dynamics such as clothing wrinkles.
arXiv Computer Vision
Aug 21
AvatarDynamizer: From Static to Dynamic Human Avatars via Generative Dynamic Textures
arXiv:2608. 19900v1 Announce Type: new Abstract: For full-body avatars, modeling surface dynamics is crucial for overcoming the uncanny valley and achieving perceptual realism.
arXiv Computer Vision
Aug 24
CogCanvas: A Benchmark for Evaluating Multi-Subject Reference-Based Image Generation
CogCanvas is a new benchmark for multi-subject reference-based image generation, featuring 1,952 curated reference images of 100 celebrities, 115 objects/fashion items, and 29 real-world backgrounds. It generates 1,361 compositional prompts with 2–5 people, using a pipeline that includes DINOv2 deduplication, aesthetic filtering, and automated graph derivation for interaction and positioning. The benchmark evaluates three tasks—reference-based multi-human-object generation, text-to-image compositional generation, and reference retrieval—under a six-axis protocol, and introduces BG‑Sim and Attr‑VQA metrics to assess background fidelity and attribute binding.
arXiv Computer Vision
Aug 31
RASA: Disentangled Spatial-Motional Priors for Cross-Identity Character Animation
arXiv:2608.28219v1 Announce Type: new Abstract: Cross-identity character animation aims to drive a target identity from a reference image to follow the motion of a source character from a driving vid...
arXiv Computer Vision
4d ago
MotionMaestro: Masked Tokenization for Unified Motion Generation
arXiv:2609.37495v1 Announce Type: new Abstract: Human motion generation plays an important role in applications such as character animation, virtual environments, and embodied interaction. While exis...
arXiv AI
Aug 24
Identity-Aware Human-Object Interaction Motion Captioning
The paper introduces the Identity-Aware Human-Object Interaction Motion Captioning task, which requires captions to include both the subject’s identity and the interaction motion, e.g., "Sub_ID lifts the chair" instead of a generic description. It proposes ID‑HOINet, a model that learns from multi‑view videos using a Multi‑View Identity‑Motion Learning Module and a Two‑Stage Caption Rewriting Strategy to generate identity‑aware captions. Experiments show that ID‑HOINet achieves state‑of‑the‑art performance on the BEHAVE and InterCap datasets.
arXiv Computer Vision
Sep 7
STyMo: Fast and Controllable Few-Shot Motion Style Transfer
STyMo is a few‑shot motion style transfer method that learns from only seconds of paired data and trains in one to two minutes. It decomposes style into a static posture component and a temporal dynamics component, allowing runtime adjustment of posture intensity, temporal exaggeration, and per‑body‑region style. The approach includes a stylizability gate to avoid artifacts on out‑of‑distribution motions and supports an iterative authoring workflow, with results shown across a range of motion styles and a released dataset for future research.
Hugging Face Trending Papers
Jul 23
GroupVideo: Multi-Identity Customized Text-to-Video Generation
Current identity customized video generation methodologies are predominantly limited to single-identity scenarios, as the lack of explicit identity separation mechanisms often leads to identity confusion in multi-identity settings. Existing multi-identity approaches, which directly extend single-identity frameworks by concatenating face images as input conditions, frequently result in unnatural facial expressions and motions, manifesting as the "copy-paste" phenomenon.