Jul 2026· SIGGRAPH Posters· pp. 1-3· 0 citations· 4 references
Computer Science
TL;DR
BlendAnything, a Blender plugin that brings cross-topology motion blending into a familiar animation workflow with a skeleton-agnostic diffusion autoencoding backend that maps motions from different skeletal structures into a learned shared per-frame latent space.
Abstract
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.
This work presents the first system that, from a single illustration, generates all the structured information a Live2D runtime consumes: ordered RGBA layers, a deformation mesh per layer, and the parameter-driven keypose vertex offsets that make the character move.
Junhao Chen, Jingjia Mao, Dayong Li et al.· arXiv.org· 0 citations
UniMate is presented, a unified foundation model that synthesizes articulated motion for arbitrary skeletons from a rigged 3D asset and a text prompt, with no test-time optimization or per-skeleton retraining, and outperforms state-of-the-art baselines in quality, generalization, and efficiency.
Lin-Zhan Mou, Jiahui Lei, Zhi-Yang Dou et al.· 0 citations
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, Ming-Ze Sun, Yifan Mao et al.· arXiv.org· 1 citation
This work introduces a framework for cross-morphology motion transfer with semantic style alignment that uses morphology-agnostic control signals (e.g., velocity, angular velocity, relative height) to align behaviors across species.
Alexios Mylordos, J. L. Pontón, Nuria Pelechano et al.· IEEE Transactions on Visuali...· 0 citations
This work presents a training-free multi-agent system that edits existing 3D meshes directly in Blender by emulating the iterative workflow of human artists, and views this work as an exploratory step toward visual-centric agentic geometry editing in professional graphics software.
Bo Pang, Jiaqi Pan, Xiao-Chen Zhang et al.· 0 citations
We introduce BLARM, a feed-forward method for video-driven 3D mesh animation. Given a monocular video and a static object mesh, BLARM predicts a temporally coherent animated mesh whose motion follows the video. Rather than relying on explicit rigs or directly regressing high-dimensional vertex motion, we represent anim...
Pradyumn Goyal, Yizhak Ben-Shabat, Hsueh-Ti Derek Liu et al.· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.