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Updated: Aug 12, 2026
09:32
Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
Published on: April 11, 2018
JointConn-v2: Learning a joint vector field with diffusion transformers for cross-modal connectivity and
Honggang Zhao1, Yi-Jun Yang1, Wei Zeng2
1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.
Summary
This study introduces JointConn-v2, improving diffusion Transformers for image-depth synthesis by enhancing cross-modal attention and stabilizing depth prediction. It balances edge control and consistency for better geometric and semantic alignment.
Area of Science:
- Computer Vision
- Deep Learning
- Generative Models
Background:
- Diffusion Transformers are used for image-depth synthesis.
- Existing methods face challenges with geometric distortions and over-coupling between semantic and depth information.
Purpose of the Study:
- To develop a unified framework, JointConn-v2, for relative-depth-conditioned and joint image-depth synthesis.
- To address bottlenecks in cross-modal attention and depth branch stability.
Main Methods:
- Proposed JointConn-v2 framework with Gated Cross-Modal Weighted Flow Matching (GCM-WFM).
- Incorporated Swap-Q cross-attention, Geometric Mask Bias, Regional Routing, and Content Gate.
- Introduced GCM-WFM for training, regressing a joint vector field with temporal, geometric, gating, and routing terms.
Main Results:
- Achieved improved balance between edge controllability and cross-modal consistency.
- Reduced geometric distortions around edges and structural regions.
- Enhanced stability in joint image-depth synthesis.
Conclusions:
- JointConn-v2 effectively improves relative-depth-conditioned and joint image-depth synthesis.
- The proposed methods mitigate key limitations of previous diffusion Transformer approaches.
- The framework offers better control over geometric details and semantic consistency.
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