6 papers
Unlocking Dense Metric Depth Estimation in VLMs
Hanxun Yu, Xuan Qu, Yuxin Wang +2
Vision-Language Models (VLMs) excel at 2D tasks such as grounding and captioning, yet remain limited in 3D understanding. A key limitation is their text-only supervision paradigm,…
Stream-R1: Reliability-Perplexity Aware Reward Distillation for Streaming Video Generation
Bin Wu, Mengqi Huang, Shaojin Wu +4
Distillation-based acceleration has become foundational for making autoregressive streaming video diffusion models practical, with distribution matching distillation (DMD) as the d…
N3D-VLM: Native 3D Grounding Enables Accurate Spatial Reasoning in Vision-Language Models
Yuxin Wang, Lei Ke, Boqiang Zhang +6
While current multimodal models can answer questions based on 2D images, they lack intrinsic 3D object perception, limiting their ability to comprehend spatial relationships and de…
One4D: Unified 4D Generation and Reconstruction via Decoupled LoRA Control
Zhenxing Mi, Yuxin Wang, Dan Xu
We present One4D, a unified framework for 4D generation and reconstruction that produces dynamic 4D content as synchronized RGB frames and pointmaps. By consistently handling varyi…
From One to More: Contextual Part Latents for 3D Generation
Shaocong Dong, Lihe Ding, Xiao Chen +10
Recent advances in 3D generation have transitioned from multi-view 2D rendering approaches to 3D-native latent diffusion frameworks that exploit geometric priors in ground truth da…
F3D-Gaus: Feed-forward 3D-aware Generation on ImageNet with Cycle-Aggregative Gaussian Splatting
Yuxin Wang, Qianyi Wu, Dan Xu
This paper tackles the problem of generalizable 3D-aware generation from monocular datasets, e.g., ImageNet. The key challenge of this task is learning a robust 3D-aware representa…