2 citations · 9 across the 22 of their papers we have counts for
24 papers
EgoPlay: Event-Triggered Video Editing for Egocentric Streams
Jinjie Mai, Gordon Guocheng Qian, Willi Menapace +8
We introduce EgoPlay, an event-triggered video-to-video editor for egocentric streams, obtained by fine-tuning a pretrained V2V diffusion transformer on event-conditioned data buil…
Cosmos 3: Omnimodal World Models for Physical AI
NVIDIA, :, Aditi +293
We introduce Cosmos 3, a family of omnimodal world models designed to jointly process and generate language, image, video, audio, and action sequences within a unified mixture-of-t…
GeoStream: Toward Precise Camera Controlled Streaming Video Generation
Yizhou Zhao, Yifan Wang, Xiaoyuan Wang +11
Accurate interactive camera control is essential for video-based world models, but most existing approaches learn camera motion implicitly, leading to inaccurate control under out-…
OmniView: An All-Seeing Diffusion Model for 3D and 4D View Synthesis
Xiang Fan, Sharath Girish, Vivek Ramanujan +6
Prior approaches injecting camera control into diffusion models have focused on specific subsets of 4D consistency tasks: novel view synthesis, text-to-video with camera control, i…
EasyV2V: A High-quality Instruction-based Video Editing Framework
Jinjie Mai, Chaoyang Wang, Guocheng Gordon Qian +5
While image editing has advanced rapidly, video editing remains less explored, facing challenges in consistency, control, and generalization. We study the design space of data, arc…
EgoEdit: Dataset, Real-Time Streaming Model, and Benchmark for Egocentric Video Editing
Runjia Li, Moayed Haji-Ali, Ashkan Mirzaei +9
We study instruction-guided editing of egocentric videos for interactive AR applications. While recent AI video editors perform well on third-person footage, egocentric views prese…
Track, Inpaint, Resplat: Subject-driven 3D and 4D Generation with Progressive Texture Infilling
Shuhong Zheng, Ashkan Mirzaei, Igor Gilitschenski
Current 3D/4D generation methods are usually optimized for photorealism, efficiency, and aesthetics. However, they often fail to preserve the semantic identity of the subject acros…
ShapeGen4D: Towards High Quality 4D Shape Generation from Videos
Jiraphon Yenphraphai, Ashkan Mirzaei, Jianqi Chen +5
Video-conditioned 4D shape generation aims to recover time-varying 3D geometry and view-consistent appearance directly from an input video. In this work, we introduce a native vide…
DELTAv2: Accelerating Dense 3D Tracking
Tuan Duc Ngo, Ashkan Mirzaei, Guocheng Qian +5
We propose a novel algorithm for accelerating dense long-term 3D point tracking in videos. Through analysis of existing state-of-the-art methods, we identify two major computationa…
4Real-Video-V2: Fused View-Time Attention and Feedforward Reconstruction for 4D Scene Generation
Chaoyang Wang, Ashkan Mirzaei, Vidit Goel +9
We propose the first framework capable of computing a 4D spatio-temporal grid of video frames and 3D Gaussian particles for each time step using a feed-forward architecture. Our ar…
DenseDPO: Fine-Grained Temporal Preference Optimization for Video Diffusion Models
Ziyi Wu, Anil Kag, Ivan Skorokhodov +5
Direct Preference Optimization (DPO) has recently been applied as a post-training technique for text-to-video diffusion models. To obtain training data, annotators are asked to pro…
3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting
Qi Wu, Janick Martinez Esturo, Ashkan Mirzaei +2
3D Gaussian Splatting (3DGS) enables efficient reconstruction and high-fidelity real-time rendering of complex scenes on consumer hardware. However, due to its rasterization-based…
EventSplat: 3D Gaussian Splatting from Moving Event Cameras for Real-time Rendering
Toshiya Yura, Ashkan Mirzaei, Igor Gilitschenski
We introduce a method for using event camera data in novel view synthesis via Gaussian Splatting. Event cameras offer exceptional temporal resolution and a high dynamic range. Leve…
Feed-Forward Bullet-Time Reconstruction of Dynamic Scenes from Monocular Videos
Hanxue Liang, Jiawei Ren, Ashkan Mirzaei +8
Recent advancements in static feed-forward scene reconstruction have demonstrated significant progress in high-quality novel view synthesis. However, these models often struggle wi…
GaussianCut: Interactive segmentation via graph cut for 3D Gaussian Splatting
Umangi Jain, Ashkan Mirzaei, Igor Gilitschenski
We introduce GaussianCut, a new method for interactive multiview segmentation of scenes represented as 3D Gaussians. Our approach allows for selecting the objects to be segmented b…
3D Gaussian Ray Tracing: Fast Tracing of Particle Scenes
Nicolas Moenne-Loccoz, Ashkan Mirzaei, Or Perel +6
Particle-based representations of radiance fields such as 3D Gaussian Splatting have found great success for reconstructing and re-rendering of complex scenes. Most existing method…
L4GM: Large 4D Gaussian Reconstruction Model
Jiawei Ren, Kevin Xie, Ashkan Mirzaei +8
We present L4GM, the first 4D Large Reconstruction Model that produces animated objects from a single-view video input -- in a single feed-forward pass that takes only a second. Ke…
RefFusion: Reference Adapted Diffusion Models for 3D Scene Inpainting
Ashkan Mirzaei, Riccardo De Lutio, Seung Wook Kim +5
Neural reconstruction approaches are rapidly emerging as the preferred representation for 3D scenes, but their limited editability is still posing a challenge. In this work, we pro…
Reconstructive Latent-Space Neural Radiance Fields for Efficient 3D Scene Representations
Tristan Aumentado-Armstrong, Ashkan Mirzaei, Marcus A. Brubaker +4
Neural Radiance Fields (NeRFs) have proven to be powerful 3D representations, capable of high quality novel view synthesis of complex scenes. While NeRFs have been applied to graph…
Watch Your Steps: Local Image and Scene Editing by Text Instructions
Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker +4
Denoising diffusion models have enabled high-quality image generation and editing. We present a method to localize the desired edit region implicit in a text instruction. We levera…
Reference-guided Controllable Inpainting of Neural Radiance Fields
Ashkan Mirzaei, Tristan Aumentado-Armstrong, Marcus A. Brubaker +4
The popularity of Neural Radiance Fields (NeRFs) for view synthesis has led to a desire for NeRF editing tools. Here, we focus on inpainting regions in a view-consistent and contro…
CAMM: Building Category-Agnostic and Animatable 3D Models from Monocular Videos
Tianshu Kuai, Akash Karthikeyan, Yash Kant +2
Animating an object in 3D often requires an articulated structure, e.g. a kinematic chain or skeleton of the manipulated object with proper skinning weights, to obtain smooth movem…
SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting with Neural Radiance Fields
Ashkan Mirzaei, Tristan Aumentado-Armstrong, Konstantinos G. Derpanis +4
Neural Radiance Fields (NeRFs) have emerged as a popular approach for novel view synthesis. While NeRFs are quickly being adapted for a wider set of applications, intuitively editi…
LaTeRF: Label and Text Driven Object Radiance Fields
Ashkan Mirzaei, Yash Kant, Jonathan Kelly +1
Obtaining 3D object representations is important for creating photo-realistic simulations and for collecting AR and VR assets. Neural fields have shown their effectiveness in learn…