187 citations · 401 across the 20 of their papers we have counts for
20 papers · 1 filter
NVIDIA OmniDreams: Real-Time Generative World Model for Closed-Loop Autonomous Vehicle Simulation
NVIDIA, :, Aarti Basant +33
As autonomous vehicle capabilities advance, the safe evaluation of driving policies in long-tail scenarios remains a critical bottleneck. In closed-loop simulation, the driving pol…
Gamma-World: Generative Multi-Agent World Modeling Beyond Two Players
Fangfu Liu, Kai He, Tianchang Shen +7
World models for interactive video generation have largely focused on single-agent settings, where future observations are generated from a single control signal. However, many gen…
Lyra 2.0: Explorable Generative 3D Worlds
Tianchang Shen, Sherwin Bahmani, Kai He +12
Recent advances in video generation enable a new paradigm for 3D scene creation: generating camera-controlled videos that simulate scene walkthroughs, then lifting them to 3D via f…
MoRight: Motion Control Done Right
Shaowei Liu, Xuanchi Ren, Tianchang Shen +5
Generating motion-controlled videos--where user-specified actions drive physically plausible scene dynamics under freely chosen viewpoints--demands two capabilities: (1) disentangl…
ArtiFixer: Enhancing and Extending 3D Reconstruction with Auto-Regressive Diffusion Models
Riccardo de Lutio, Tobias Fischer, Yen-Yu Chang +7
Per-scene optimization methods such as 3D Gaussian Splatting provide state-of-the-art novel view synthesis quality but extrapolate poorly to under-observed areas. Methods that leve…
Depth Completion as Parameter-Efficient Test-Time Adaptation
Bingxin Ke, Qunjie Zhou, Jiahui Huang +5
We introduce CAPA, a parameter-efficient test-time optimization framework that adapts pre-trained 3D foundation models (FMs) for depth completion, using sparse geometric cues. Unli…
ChronoEdit: Towards Temporal Reasoning for Image Editing and World Simulation
Jay Zhangjie Wu, Xuanchi Ren, Tianchang Shen +11
Recent advances in large generative models have greatly enhanced both image editing and in-context image generation, yet a critical gap remains in ensuring physical consistency, wh…
Lyra: Generative 3D Scene Reconstruction via Video Diffusion Model Self-Distillation
Sherwin Bahmani, Tianchang Shen, Jiawei Ren +10
The ability to generate virtual environments is crucial for applications ranging from gaming to physical AI domains such as robotics, autonomous driving, and industrial AI. Current…
ViPE: Video Pose Engine for 3D Geometric Perception
Jiahui Huang, Qunjie Zhou, Hesam Rabeti +12
Accurate 3D geometric perception is an important prerequisite for a wide range of spatial AI systems. While state-of-the-art methods depend on large-scale training data, acquiring…
Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models
Xuanchi Ren, Yifan Lu, Tianshi Cao +13
Collecting and annotating real-world data for safety-critical physical AI systems, such as Autonomous Vehicle (AV), is time-consuming and costly. It is especially challenging to ca…
Cosmos-Transfer1: Conditional World Generation with Adaptive Multimodal Control
NVIDIA, :, Hassan Abu Alhaija +38
We introduce Cosmos-Transfer, a conditional world generation model that can generate world simulations based on multiple spatial control inputs of various modalities such as segmen…
GEN3C: 3D-Informed World-Consistent Video Generation with Precise Camera Control
Xuanchi Ren, Tianchang Shen, Jiahui Huang +7
We present GEN3C, a generative video model with precise Camera Control and temporal 3D Consistency. Prior video models already generate realistic videos, but they tend to leverage…
InfiniCube: Unbounded and Controllable Dynamic 3D Driving Scene Generation with World-Guided Video Models
Yifan Lu, Xuanchi Ren, Jiawei Yang +8
We present InfiniCube, a scalable method for generating unbounded dynamic 3D driving scenes with high fidelity and controllability. Previous methods for scene generation either suf…
SpaceMesh: A Continuous Representation for Learning Manifold Surface Meshes
Tianchang Shen, Zhaoshuo Li, Marc Law +5
Meshes are ubiquitous in visual computing and simulation, yet most existing machine learning techniques represent meshes only indirectly, e.g. as the level set of a scalar field or…
Adaptive Shells for Efficient Neural Radiance Field Rendering
Zian Wang, Tianchang Shen, Merlin Nimier-David +6
Neural radiance fields achieve unprecedented quality for novel view synthesis, but their volumetric formulation remains expensive, requiring a huge number of samples to render high…
Neural Fields meet Explicit Geometric Representation for Inverse Rendering of Urban Scenes
Zian Wang, Tianchang Shen, Jun Gao +6
Reconstruction and intrinsic decomposition of scenes from captured imagery would enable many applications such as relighting and virtual object insertion. Recent NeRF based methods…
GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images
Jun Gao, Tianchang Shen, Zian Wang +6
As several industries are moving towards modeling massive 3D virtual worlds, the need for content creation tools that can scale in terms of the quantity, quality, and diversity of…
Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis
Tianchang Shen, Jun Gao, Kangxue Yin +2
We introduce DMTet, a deep 3D conditional generative model that can synthesize high-resolution 3D shapes using simple user guides such as coarse voxels. It marries the merits of im…
Extracting Triangular 3D Models, Materials, and Lighting From Images
Jacob Munkberg, Jon Hasselgren, Tianchang Shen +5
We present an efficient method for joint optimization of topology, materials and lighting from multi-view image observations. Unlike recent multi-view reconstruction approaches, wh…
Interactive Annotation of 3D Object Geometry using 2D Scribbles
Tianchang Shen, Jun Gao, Amlan Kar +1
Inferring detailed 3D geometry of the scene is crucial for robotics applications, simulation, and 3D content creation. However, such information is hard to obtain, and thus very fe…