arXiv Machine Learning June 8, 2026 By Dian Gu, Zhengyi Yang

TrioPose: Native Triple-Stream Diffusion Transformers for Pose-Guided Text-to-Image Generation

Read the original on arXiv Machine Learning →

arXiv:2606. 07053v1 Announce Type: cross Abstract: Pose-guided text-to-image generation often suffers from limb distortions and feature crosstalk in complex multi-person scenarios.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv AI

Jun 30

UniMotion: A Unified Framework for Motion-Text-Vision Understanding and Generation

arXiv:2603. 22282v2 Announce Type: replace-cross Abstract: We present UniMotion, to our knowledge the first unified framework for simultaneous understanding and generation of human motion, natural language, and RGB images within a single architecture.

By Ziyi Wang, Xinshun Wang, Shuang Chen, Yang Cong, Mengyuan Liu

llms nlp efficiency multimodal benchmarks safety

arXiv AI

Aug 11

Compositional Cross-Modality Translation via Whole-Volume Multitask Latent Flow Matching

arXiv:2608. 08135v1 Announce Type: cross Abstract: Cross-modality medical image translation can reduce the burden of multi-modal acquisitions, yet the field remains constrained by two coupled limitations: methods operate on 2D slices or 3D patches rather than whole volumes, and train a separate model for each translation task.

By Daniele Molino, Alessio Zoboli, Camillo Maria Caruso, Valerio Guarrasi, Paolo Soda

diffusion multimodal

arXiv Computer Vision

Sep 23

Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene Reconstruction

arXiv:2609.23796v2 Announce Type: replace Abstract: Single-image 3D object generation can now produce high-fidelity assets, yet accurately placing them into a coherent scene layout remains an open ch...

By Yang-Tian Sun, Tianjia Liu, Zehuan Huang, Yi-Hua Huang, Xiaoyang Lyu, Ziyi Yang, Zi-Xin Zou, Yuan-Chen Guo, Yan-Pei Cao, Xiaojuan Qi

llms diffusion multimodal safety

arXiv Computer Vision

Sep 22

Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene

arXiv:2609.23796v1 Announce Type: new Abstract: Single-image 3D object generation can now produce high-fidelity assets, yet accurately placing them into a coherent scene layout remains an open challe...

By Yang-Tian Sun, Tianjia Liu, Zehuan Huang, Yi-Hua Huang, Xiaoyang Lyu, Ziyi Yang, Zi-Xin Zou, Yuan-Chen Guo, Yan-Pei Cao, Xiaojuan Qi

llms diffusion multimodal safety

arXiv Computer Vision

Sep 3

Make-It-Poseable: Feed-forward Latent Posing Model for 3D Characters

Make‑It‑Poseable is a feed‑forward framework that treats 3D character posing as a skinning‑free latent‑space transformation. It decouples shape deformation from fixed mesh connectivity, using a latent posing transformer, dense pose representation, and an adaptive completion module with bipartite‑matched latent loss. Experiments show it outperforms existing baselines, generalizes to varied morphologies, and supports 3D authoring tasks such as part replacement and refinement.

By Zhiyang Guo, Ori Zhang, Jax Xiang, Alan Zhao, Zhenxun Yuan, Wengang Zhou, Houqiang Li

llms robotics

arXiv AI

Jun 10

Pose-ICL: 3D-Aware In-Context Learning for Pose-Controllable Subject Customization

arXiv:2606. 10902v1 Announce Type: cross Abstract: Subject Customization is a foundational task in modern image generation.

By Xuan Han, Yihao Zhao, Mingyu You

llms rag diffusion

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UniMotion: A Unified Framework for Motion-Text-Vision Understanding and Generation

Compositional Cross-Modality Translation via Whole-Volume Multitask Latent Flow Matching

Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene Reconstruction

Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene

Make-It-Poseable: Feed-forward Latent Posing Model for 3D Characters

Pose-ICL: 3D-Aware In-Context Learning for Pose-Controllable Subject Customization