Jul 2026· ACM Transactions on Graphics· Vol 45, pp. 1 - 20· 0 citations· 65 references
Computer Science
TL;DR
ACT is introduced, a unified generative framework that reformulates rigging and animation not as independent tasks, but as complementary views of a single hyper-kinematic process, offering a versatile and efficient solution for automating 3D character animation.
Abstract
Recent advances in generative models have democratized the creation of high-quality static 3D assets, yet animating these meshes remains a labor-intensive bottleneck. Traditional pipelines fracture this process into sequential stages—rigging, skinning, and motion synthesis—ignoring the inherent coupling between morphological structure and motor function. To bridge this gap, we introduce ACT, a unified generative framework that reformulates rigging and animation not as independent tasks, but as complementary views of a single hyper-kinematic process. Our key insight is to model the joint distribution of skeletal topology and temporal motion within a shared latent space. ACT utilizes a Vision Language Model (VLM) to extract semantic topological priors from arbitrary meshes, which then condition a Diffusion Transformer (DiT) backbone. By treating static rest poses and dynamic trajectories as a unified sequence, our model employs a task-aware masking strategy to flexibly perform zero-shot rigging, text-guided motion generation, and motion completion within a single end-to-end architecture. Furthermore, a geometry-guided decoder ensures that surface deformations are tightly coupled with the generated kinematics. Extensive experiments demonstrate that ACT generalizes robustly to diverse, non-humanoid characters without retraining. By replacing brittle cascaded pipelines with a holistic prior, our method enables novel applications such as semantic-driven topology editing and generative in-betweening, offering a versatile and efficient solution for automating 3D character animation.
ViP-Rig is a visual-prompted framework that supports both prompt-first rigging and result-guided editing by injecting features extracted from user-drawn or edited 2D skeletal and rigidity prompts into frozen pretrained backbones into a frozen pretrained autoregressive generator.
Zihan Qin, Mingze Sun, Yifan Mao et al.· 0 citations
Animating articulated 3D meshes via text requires satisfying strict kinematic constraints, modeling causal interactions between parts, and achieving instruction fidelity. Due to the absence of task-specific training data and explicit articulation supervision, existing data-driven mesh animation methods are largely inapplicable to this setting. To address this, we propose ArtiMo, a novel agent-driven framework for text-guided articulated mesh animation. Operating in a zero-shot manner, ArtiMo develops an agentic pipeline powered by Large Language and Vision-Language Models (LLMs/VLMs) to orchestrate motion generation. By synergizing the explicit kinematic constraints of URDF with the agent's reasoning and planning capabilities, it effectively produces causally coherent part motions and interactions without requiring model fine-tuning. To ensure motion correctness, the agent additionally utilizes a visual self-improvement mechanism: generated animations are rendered into compact keyframes and motion cues, enabling the VLM to iteratively diagnose and correct errors. Furthermore, we contribute a new benchmark dataset spanning 21 articulated object categories, featuring high-quality motion annotations enriched with causal relationships. Extensive experiments demonstrate that ArtiMo significantly outperforms baselines, particularly on complex, causally driven motions. The project page is available at https://zou-2004.github.io/ArtiMo/.
Chunyu Zou, Peng Dai, Yi-Hua Huang et al.· 0 citations
The recent success of generative modeling has led to entirely new capabilities to author 3D motion. By manipulating only a few sparse handles and poses, it is now possible to generate entire motion sequences, at scale and with many variations. To bridge the gap between model research and real-world integration, we developed a Generative Motion Rig as a Blender plugin, built atop a general motion model. Our rig supports a new “generative keyframing” workflow, where artists author movements by manipulating sparse poses, handles, window lengths, and noise sampling. We show in our accompanying video how artists use these controls to rapidly make a short animation. We also show how the same rig can support generative motion editing, allowing one to edit and extend mocap clips. Finally, we share insights and future challenges to help close the gap between generative and traditional animation workflows.
Jakob Buhmann, Dhruv Agrawal, D. Borer et al.· Proceedings of the Special I...· 0 citations
Motion blending remains a core tool in character animation, yet standard workflows are still largely constrained by fixed skeleton representations. When characters differ in topology, proportions, or hierarchy, artists often need manual correspondence design, retargeting, and substantial cleanup before they can explore even simple blended motions. We present BlendAnything, a Blender plugin that brings cross-topology motion blending into a familiar animation workflow. The plugin is powered by a skeleton-agnostic diffusion autoencoding backend that maps motions from different skeletal structures into a learned shared per-frame latent space, where high-level motion attributes such as action, phase, and global pose dynamics can be interpolated independently of rig topology, and then decodes the result into motion that remains structurally compatible with the chosen output skeleton. This design allows users to select reference and target motions, control the transition, and preview cross-topology blends directly inside Blender. We demonstrate the approach on the Truebones Zoo dataset with both quantitative and qualitative results in in-skeleton and cross-skeleton settings. By embedding this capability into Blender, BlendAnything turns cross-topology motion blending into a practical authoring tool for animation.
Recent advances in 4D content generation have attracted increasing attention, yet creating high-quality animated 3D models remains challenging due to the complexity of modeling spatio-temporal distributions and the scarcity of 4D training data. We present AnimateAnyMesh++, a feed-forward framework for text-driven animation of arbitrary 3D meshes with substantial upgrades in data, architecture, and generative capability. First, we expand the DyMesh-XL dataset by mining dynamic content from Objaverse-XL, increasing the number of unique identities from 60K to 300K and substantially broadening category and motion diversity. Second, we redesign DyMeshVAE-Flex with power-law topology-aware attention and vertex-normal-enhanced features, which significantly improves trajectory reconstruction, local geometry preservation, and mit igates trajectory-sticking artifacts. Third, we introduce archi tectural changes to both DyMeshVAE-Flex and the rectified flow (RF) generator to support variable-length sequence training and generation, enabling longer animations while preserving reconstruction fidelity. Extensive experiments demonstrate that AnimateAnyMesh++ generates semantically accurate and tem porally coherent mesh animations within seconds, surpassing prior approaches in quality and efficiency. The enlarged DyMesh XL, the upgraded DyMeshVAE-Flex, and variable-length RF to gether deliver consistent gains across benchmarks and in-the-wild meshes. We will release code, models, and the expanded DyMesh XL at https://github.com/JarrentWu1031/AnimateAnyMesh-pp upon acceptance of this manuscript to facilitate research in 4D content creation.
Zijie Wu, Chaohui Yu, Fan Wang et al.· IEEE Transactions on Pattern...· 2 citations· ⚡1
FlexComposer is proposed, a unified framework that standardizes video compositing as a trajectory-guided conditional generation task, enabling the seamless integration of both static images and dynamic footage.
Song-Chun Zhang, S. Guo, Xianghao Kong et al.· 0 citations