5 papers
A training-free framework for high-fidelity appearance transfer via diffusion transformers
Shengrong Gu, Ye Wang, Song Wu +4
Diffusion Transformers (DiTs) excel at generation, but their global self-attention makes controllable, reference-image-based editing a distinct challenge. Unlike U-Nets, naively in…
FreeControl: Efficient, Training-Free Structural Control via One-Step Attention Extraction
Jiang Lin, Xinyu Chen, Song Wu +7
Controlling the spatial and semantic structure of diffusion-generated images remains a challenge. Existing methods like ControlNet rely on handcrafted condition maps and retraining…
DGS-LRM: Real-Time Deformable 3D Gaussian Reconstruction From Monocular Videos
Chieh Hubert Lin, Zhaoyang Lv, Songyin Wu +11
We introduce the Deformable Gaussian Splats Large Reconstruction Model (DGS-LRM), the first feed-forward method predicting deformable 3D Gaussian splats from a monocular posed vide…
Monocular Online Reconstruction with Enhanced Detail Preservation
Songyin Wu, Zhaoyang Lv, Yufeng Zhu +6
We propose an online 3D Gaussian-based dense mapping framework for photorealistic details reconstruction from a monocular image stream. Our approach addresses two key challenges in…
E-Motion: Future Motion Simulation via Event Sequence Diffusion
Song Wu, Zhiyu Zhu, Junhui Hou +2
Forecasting a typical object's future motion is a critical task for interpreting and interacting with dynamic environments in computer vision. Event-based sensors, which could capt…