24 citations · 27 across the 25 of their papers we have counts for
29 papers
VeriFine: Scaling Verification for Self-Improvement in Embodied Reasoning
Zewei Zhou, Rachel Luo, Yulong Cao +10
Self-improving policies continually expose new failure patterns, changing what their judges must be able to verify. However, current fixed judges constrain both optimization feedba…
CtrlWAM: Controllable World Action Models with Aligned Intent and Foresight
Chensheng Peng, Wenhao Ding, Ran Tian +9
World action models (WAMs) jointly predict actions (intent) and visual future (foresight). Standard training adds noise to recorded actions and video simultaneously, but such train…
RoboSTAR: Next-Scale Autoregressive Sign Language Translation for Humanoid Robots
Yujia Zeng, Chensheng Peng, Yuxin Chen +3
Sign-language interpretation in public communication relies on qualified professional interpreters and can be difficult to scale, motivating robotic signing as a complementary acce…
Towards Interactive Video World Modeling: Frontiers, Challenges, Benchmarks, and Future Trends
Jiuming Liu, Chaojun Ni, Mengmeng Liu +7
With rapid development of large language models and diffusion-based content generation, world modeling has attracted increasing research attention, benefiting various downstream do…
Teaching Video Generators to Remember: Eliciting Dynamic Memory for Out-of-Sight State Evolution
Tianshuo Xu, Yichen Xie, Depu Meng +5
Video world models should maintain evolving states when evidence is unobserved, yet current generators often freeze hidden states upon interruption. This is not simply a capacity p…
Rethinking Image-to-3D Generation with Sparse Queries: Efficiency, Capacity, and Input-View Bias
Zhiyuan Xu, Jiuming Liu, Yuxin Chen +3
We present SparseGen, a novel framework for efficient image-to-3D generation, which exhibits low input-view bias while being significantly faster. Unlike traditional approaches tha…
SpectralSplat: Appearance-Disentangled Feed-Forward Gaussian Splatting for Driving Scenes
Quentin Herau, Tianshuo Xu, Depu Meng +5
Feed-forward 3D Gaussian Splatting methods have achieved impressive reconstruction quality for autonomous driving scenes, yet they entangle scene geometry with transient appearance…
DiscreteRTC: Discrete Diffusion Policies are Natural Asynchronous Executors
Pengcheng Wang, Kaiwen Hong, Chensheng Peng +4
Unlike chatbots, physical AI must act while the world keeps evolving. Therefore, the inter-chunk pause of synchronous executors are fatal for dynamic tasks regardless of how fast t…
URoPE: Universal Relative Position Embedding across Geometric Spaces
Yichen Xie, Depu Meng, Chensheng Peng +4
Relative position embedding has become a standard mechanism for encoding positional information in Transformers. However, existing formulations are typically limited to a fixed geo…
RT-GS: Gaussian Splatting with Reflection and Transmittance Primitives
Kunnong Zeng, Chensheng Peng, Yichen Xie +2
Gaussian Splatting is a powerful tool for reconstructing diffuse scenes, but it struggles to simultaneously model specular reflections and the appearance of objects behind semi-tra…
Learning to Drive is a Free Gift: Large-Scale Label-Free Autonomy Pretraining from Unposed In-The-Wild Videos
Matthew Strong, Wei-Jer Chang, Quentin Herau +4
Ego-centric driving videos available online provide an abundant source of visual data for autonomous driving, yet their lack of annotations makes it difficult to learn representati…
UniQueR: Unified Query-based Feedforward 3D Reconstruction
Chensheng Peng, Quentin Herau, Jiezhi Yang +6
We present UniQueR, a unified query-based feedforward framework for efficient and accurate 3D reconstruction from unposed images. Existing feedforward models such as DUSt3R, VGGT,…
RAYNOVA: Scale-Temporal Autoregressive World Modeling in Ray Space
Yichen Xie, Chensheng Peng, Mazen Abdelfattah +6
World foundation models aim to simulate the evolution of the real world with physically plausible behavior. Unlike prior methods that handle spatial and temporal correlations separ…
DIAL-GS: Dynamic Instance Aware Reconstruction for Label-free Street Scenes with 4D Gaussian Splatting
Chenpeng Su, Wenhua Wu, Chensheng Peng +3
Urban scene reconstruction is critical for autonomous driving, enabling structured 3D representations for data synthesis and closed-loop testing. Supervised approaches rely on cost…
VER: Vision Expert Transformer for Robot Learning via Foundation Distillation and Dynamic Routing
Yixiao Wang, Mingxiao Huo, Zhixuan Liang +8
Pretrained vision foundation models (VFMs) advance robotic learning via rich visual representations, yet individual VFMs typically excel only in specific domains, limiting generali…
S2GO: Streaming Sparse Gaussian Occupancy Prediction
Jinhyung Park, Yihan Hu, Chensheng Peng +3
Despite the demonstrated efficiency and performance of sparse query-based representations for perception, state-of-the-art 3D occupancy prediction methods still rely on voxel-based…
R3D2: Realistic 3D Asset Insertion via Diffusion for Autonomous Driving Simulation
William Ljungbergh, Bernardo Taveira, Wenzhao Zheng +8
Validating autonomous driving (AD) systems requires diverse and safety-critical testing, making photorealistic virtual environments essential. Traditional simulation platforms, whi…
BEV-GS: Feed-forward Gaussian Splatting in Bird's-Eye-View for Road Reconstruction
Wenhua Wu, Tong Zhao, Chensheng Peng +4
Road surface is the sole contact medium for wheels or robot feet. Reconstructing road surface is crucial for unmanned vehicles and mobile robots. Recent studies on Neural Radiance…
A Lesson in Splats: Teacher-Guided Diffusion for 3D Gaussian Splats Generation with 2D Supervision
Chensheng Peng, Ido Sobol, Masayoshi Tomizuka +3
We present a novel framework for training 3D image-conditioned diffusion models using only 2D supervision. Recovering 3D structure from 2D images is inherently ill-posed due to the…
X-Drive: Cross-modality consistent multi-sensor data synthesis for driving scenarios
Yichen Xie, Chenfeng Xu, Chensheng Peng +6
Recent advancements have exploited diffusion models for the synthesis of either LiDAR point clouds or camera image data in driving scenarios. Despite their success in modeling sing…
DeSiRe-GS: 4D Street Gaussians for Static-Dynamic Decomposition and Surface Reconstruction for Urban Driving Scenes
Chensheng Peng, Chengwei Zhang, Yixiao Wang +6
We present DeSiRe-GS, a self-supervised gaussian splatting representation, enabling effective static-dynamic decomposition and high-fidelity surface reconstruction in complex drivi…
CompGS: Unleashing 2D Compositionality for Compositional Text-to-3D via Dynamically Optimizing 3D Gaussians
Chongjian Ge, Chenfeng Xu, Yuanfeng Ji +6
Recent breakthroughs in text-guided image generation have significantly advanced the field of 3D generation. While generating a single high-quality 3D object is now feasible, gener…
Optimizing Diffusion Models for Joint Trajectory Prediction and Controllable Generation
Yixiao Wang, Chen Tang, Lingfeng Sun +8
Diffusion models are promising for joint trajectory prediction and controllable generation in autonomous driving, but they face challenges of inefficient inference steps and high c…
Joint Pedestrian Trajectory Prediction through Posterior Sampling
Haotian Lin, Yixiao Wang, Mingxiao Huo +3
Joint pedestrian trajectory prediction has long grappled with the inherent unpredictability of human behaviors. Recent investigations employing variants of conditional diffusion mo…