TrioPose: Native Triple-Stream Diffusion Transformers for Pose-Guided Text-to-Image Generation
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. While existing UNet-based adapters struggle with long-range spatial dependencies, emerging Multimodal Diffusion Transformers (MM-DiTs) offer superior global modeling. However, naive signal concatenation in MM-DiTs severely disrupts pre-trained latent distributions. To address this, we propose TrioPose, a native pose-driven framework built upon the SD3.5M architecture. Specifically, we introduce a Triple-Stream Pose-A
The continuous evolution of diffusion models and transformer architectures enables new research into overcoming current limitations in realistic image generation.
Improved pose-guided image generation, especially for complex multi-person scenarios, significantly enhances digital content creation, virtual reality, and AI agent interaction.
The proposed TrioPose framework, based on SD3.5M, provides a more robust solution for generating accurate human poses in text-to-image models, reducing common distortions.
- · AI content creators
- · Gaming industry
- · Virtual reality developers
- · Generative AI platforms
- · Legacy image generation techniques
- · Platforms with poor pose consistency
More realistic and controllable human figures will appear in AI-generated imagery and video.
This capability could accelerate the development of personalized virtual avatars and digital fashion applications.
The integration of such sophisticated image generation could lead to new forms of immersive storytelling and interactive media experiences.
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Read at arXiv cs.LG