In-betweening goes neural, billion-parameter motion models go open, and the embodied AI conference calendar fills up
From the arXiv
Generative Motion In-betweening by Diffusion over Continuous Implicit Representations (arXiv:2605.12778, May 12) Fan, Henderson, & Ho (University of Glasgow) propose encoding motion as implicit neural representations and using latent diffusion to sample plausible, diverse in-between sequences from sparse keyframe constraints. The INR approach allows the model to generate motion at arbitrary temporal resolution — not locked to training frame rate. Strong results on sparse-keyframe in-betweening quality and diversity. Relevant for score-based and phrase-structured somatic co-creation workflows. → https://arxiv.org/abs/2605.12778
UMo: Unified Sparse Motion Modeling for Real-Time Co-Speech Avatars (arXiv:2605.14731, May 14) Zhan et al. unify text, audio, and motion as discrete tokens in a single transformer, achieving tight audio–body gesture alignment at real-time throughput. The unified token approach removes the need for separate alignment modules per modality. Notable step toward multi-channel conditioning systems where speech, gesture, and movement are co-modelled rather than processed in parallel independent pipelines. → https://arxiv.org/abs/2605.14731
Riemannian Motion Generation via Flow Matching (arXiv:2603.15016, March — CVPR 2026 accepted) Miao, Huang, & Li (PolyU / SUSTech) represent motion on a product manifold and learn dynamics via Riemannian flow matching. State-of-the-art FID of 0.043 on HumanML3D; first place on all metrics in MotionStreamer format. This paper is appearing in CVPR 2026 proceedings, bringing manifold-aware motion representation into the mainstream conference canon. Geometrically principled encoding — relevant to the qualitative texture discussion from the May 1 deep analysis. → https://arxiv.org/abs/2603.15016
From HuggingFace
Tencent HY-Motion 1.0 — Tencent released HY-Motion 1.0 to HuggingFace this week: a series of text-to-3D human motion generation models based on Diffusion Transformer (DiT) + flow matching, scaled to the billion-parameter level. The first open-source text-to-motion model family at this scale. Generates skeleton-based 3D animation from text prompts with notably improved instruction-following over smaller open models. Available for community fine-tuning. → https://huggingface.co/tencent/HY-Motion-1.0
From Import AI
Import AI #457 (Jack Clark, May 18, 2026) — This week's edition covers: an investigation into fast16.sys, a ~20-year-old computer virus that selectively patches high-precision calculation software in memory to tamper with numerical results — a quiet threat to scientific computation; research on pathologies in the Muon optimizer (more than one in four neurons effectively dead by training step 500 under certain configurations); and a multi-institution paper on positive alignment — defining AI development that supports human and ecological flourishing in pluralistic, user-authored ways. The alignment paper's co-signatories include Oxford, Google DeepMind, Anthropic, OpenAI, and Stanford. → https://jack-clark.net/2026/05/18/import-ai-457-ai-stuxnet-cursed-muon-optimizer-and-positive-alignment/
From X / Social
@Marktechpost shared DanceGRPO (ByteDance Seed + Univ. Hong Kong) — a unified framework adapting Group Relative Policy Optimization (GRPO) to visual generation. Applied across diffusion and rectified flow paradigms, text-to-image, text-to-video, and image-to-video, with reward types including motion quality. The application of RL-style reward shaping to motion quality in generation pipelines is a technically interesting direction: motion quality rewards could eventually be defined in terms of somatic criteria rather than visual plausibility.
@Kling_ai announced new tutorial content for Kling 2.6 Motion Control — uploading a character image plus an original dance or expression video to generate a new combination. Practitioner-facing tool; the reference-video-to-new-character transfer workflow is directly usable for exploring how movement vocabulary transfers across body types and visual styles.
@DrTomFroese confirmed funding for the International Conference on Embodied Cognitive Science (ECogS 2026) at OIST, Nov. 9–13, 2026. Theme: Embodied cognition and AI. This is a significant conference for the phenomenological and cognitive-scientific grounding of somatic-AI research — abstract submissions likely to open over the coming weeks.
Conference Notes
HuMoGen Workshop @ CVPR 2026 — The Human Motion Generation workshop is confirmed for CVPR 2026 (Nashville, June). Inviting accepted conference and journal papers in motion generation, with poster and presentation tracks. CVPR 2026 as a whole accepted 4,090 papers (25% acceptance rate, up 42% from prior year), with a dedicated oral session on Human Motion. The scale of CVPR 2026 motion research suggests the field has fully entered the mainstream CV/ML canon.
Foundation Models Meet Embodied Agents (FMEA) Workshop @ CVPR 2026 — Four challenges with 300/$200 cash prizes per track, covering VLMs, LLM planning, and VLA policies. Embodied AI challenge submissions currently open.
Continue through the archive
For connected context, read This Week in Motion AI: In-betweening, Co-Speech Avatars, and Billion-Parameter Motion Models and The Movement Alphabet That's Teaching AI How Bodies Actually Feel.
References
Fan, S., Henderson, P., & Ho, E. S. L. (2026). Generative motion in-betweening by diffusion over continuous implicit representations. arXiv:2605.12778. https://arxiv.org/abs/2605.12778
Miao, F., Huang, J., & Li, T. (2026). Riemannian motion generation: A unified framework for human motion representation and generation via Riemannian flow matching. arXiv:2603.15016. https://arxiv.org/abs/2603.15016
Tencent. (2026). HY-Motion-1.0 [Model repository]. HuggingFace. https://huggingface.co/tencent/HY-Motion-1.0
Zhan, X., Fu, X., Yang, C., et al. (2026). UMo: Unified sparse motion modeling for real-time co-speech avatars. arXiv:2605.14731. https://arxiv.org/abs/2605.14731