Author name cluster

Chaoyang Wang 0001

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

6 papers

1 author row

ICLR Conference 2025 Conference Paper

3DitScene: Editing Any Scene via Language-guided Disentangled Gaussian Splatting

Qihang Zhang
Yinghao Xu 0001
Chaoyang Wang 0001
Hsin-Ying Lee 0001
Gordon Wetzstein
Bolei Zhou
Ceyuan Yang

Scene image editing is crucial for entertainment, photography, and advertising design. Existing methods solely focus on either 2D individual object or 3D global scene editing. This results in a lack of a unified approach to effectively control and manipulate scenes at the 3D level with different levels of granularity. In this work, we propose 3DitScene, a novel and unified scene editing framework leveraging language-guided disentangled Gaussian Splatting that enables seamless editing from 2D to 3D, allowing precise control over scene composition and individual objects. We first incorporate 3D Gaussians that are refined through generative priors and optimization techniques. Language features from CLIP then introduce semantics into 3D geometry for object disentanglement. With the disentangled Gaussians, 3DitScene allows for manipulation at both the global and individual levels, revolutionizing creative expression and empowering control over scenes and objects. Experimental results demonstrate the effectiveness and versatility of 3DitScene in scene image editing.

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ICLR Conference 2025 Conference Paper

Delta: Dense Efficient Long-Range 3D tracking for any video

Tuan Duc Ngo
Peiye Zhuang
Evangelos Kalogerakis
Chuang Gan 0001
Sergey Tulyakov
Hsin-Ying Lee 0001
Chaoyang Wang 0001

Tracking dense 3D motion from monocular videos remains challenging, particularly when aiming for pixel-level precision over long sequences. We introduce DELTA, a novel method that efficiently tracks every pixel in 3D space, enabling accurate motion estimation across entire videos. Our approach leverages a joint global-local attention mechanism for reduced-resolution tracking, followed by a transformer-based upsampler to achieve high-resolution predictions. Unlike existing methods, which are limited by computational inefficiency or sparse tracking, DELTA delivers dense 3D tracking at scale, running over 8x faster than previous methods while achieving state-of-the-art accuracy. Furthermore, we explore the impact of depth representation on tracking performance and identify log-depth as the optimal choice. Extensive experiments demonstrate the superiority of DELTA on multiple benchmarks, achieving new state-of-the-art results in both 2D and 3D dense tracking tasks. Our method provides a robust solution for applications requiring fine-grained, long-term motion tracking in 3D space.

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ICLR Conference 2025 Conference Paper

GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement

Peiye Zhuang
Songfang Han
Chaoyang Wang 0001
Aliaksandr Siarohin
Jiaxu Zou
Michael Vasilkovsky
Vladislav Shakhrai
Sergei Korolev

We propose a novel approach for 3D mesh reconstruction from multi-view images. We improve upon the large reconstruction model LRM that use a transformer-based triplane generator and a Neural Radiance Field (NeRF) model trained on multi-view images. We introduce three key components to significantly enhance the 3D reconstruction quality. First of all, we examine the original LRM architecture and find several shortcomings. Subsequently, we introduce respective modifications to the LRM architecture, which lead to improved multi-view image representation and more computationally efficient training. Second, in order to improve geometry reconstruction and enable supervision at full image resolution, we extract meshes from the NeRF in a differentiable manner and fine-tune the NeRF model through mesh rendering. These modifications allow us to achieve state-of-the-art performance on both 2D and 3D evaluation metrics on Google Scanned Objects (GSO) dataset and OmniObject3D dataset. Finally, we introduce a lightweight per-instance texture refinement procedure to better reconstruct complex textures, such as text and portraits on assets. To address this, we introduce a lightweight per-instance texture refinement procedure. This procedure fine-tunes the triplane representation and the NeRF's color estimation model on the mesh surface using the input multi-view images in just 4 seconds. This refinement achieves faithful reconstruction of complex textures. Additionally, our approach enables various downstream applications, including text/image-to-3D generation.

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ICLR Conference 2025 Conference Paper

Lightweight Predictive 3D Gaussian Splats

Junli Cao
Vidit Goel
Chaoyang Wang 0001
Anil Kag
Ju Hu
Sergei Korolev
Chenfanfu Jiang
Sergey Tulyakov

Recent approaches representing 3D objects and scenes using Gaussian splats show increased rendering speed across a variety of platforms and devices. While rendering such representations is indeed extremely efficient, storing and transmitting them is often prohibitively expensive. To represent large-scale scenes, one often needs to store millions of 3D Gaussian, which can occupy up to gigabytes of storage. This creates a significant practical barrier, preventing widespread adoption on resource-constrained devices. In this work, we propose a new representation that dramatically reduces the hard drive footprint while featuring similar or improved quality when compared to the standard 3D Gaussian splats. This representation leverages the inherent feature sharing among splats in the close proximity using a hierarchical tree structure, with which only the parent splats need to be stored. We present a method for constructing tree structures from naturally unstructured point clouds. Additionally, we propose the adaptive tree manipulation to prune the redundant trees in the space, while spawn new ones from the significant children splats during the optimization process. On the benchmark datasets, we achieve 20x storage reduction in hard-drive footprint with improved fidelity compared to the vanilla 3DGS and 2-5x reduction compared to the exiting compact solutions. More importantly, we demonstrate the practical application of our method in real-world rendering on mobile devices and AR glasses.

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ICLR Conference 2025 Conference Paper

VD3D: Taming Large Video Diffusion Transformers for 3D Camera Control

Sherwin Bahmani
Ivan Skorokhodov
Aliaksandr Siarohin
Willi Menapace
Guocheng Qian
Michael Vasilkovsky
Hsin-Ying Lee 0001
Chaoyang Wang 0001

Modern text-to-video synthesis models demonstrate coherent, photorealistic generation of complex videos from a text description. However, most existing models lack fine-grained control over camera movement, which is critical for downstream applications related to content creation, visual effects, and 3D vision. Recently, new methods demonstrate the ability to generate videos with controllable camera poses---these techniques leverage pre-trained U-Net-based diffusion models that explicitly disentangle spatial and temporal generation. Still, no existing approach enables camera control for new, transformer-based video diffusion models that process spatial and temporal information jointly. Here, we propose to tame video transformers for 3D camera control using a ControlNet-like conditioning mechanism that incorporates spatiotemporal camera embeddings based on Plucker coordinates. The approach demonstrates state-of-the-art performance for controllable video generation after fine-tuning on the RealEstate10K dataset. To the best of our knowledge, our work is the first to enable camera control for transformer-based video diffusion models.

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ICRA Conference 2018 Conference Paper

Deep-LK for Efficient Adaptive Object Tracking

Chaoyang Wang 0001
Hamed Kiani Galoogahi
Chen-Hsuan Lin 0001
Simon Lucey

In this paper, we present a new approach for efficient regression-based object tracking. Our approach is closely related to the Generic Object Tracking Using Regression Networks (GOTURN) framework [1]. We make the following contributions. First, we demonstrate that there is a theoretical relationship between Siamese regression networks like GOTURN and the classical Inverse Compositional Lucas & Kanade (IC-LK) algorithm. Further, we demonstrate that unlike GOTURN, IC-LK adapts its regressor to the appearance of the current tracked frame. We argue that the lack of such property in GOTURN attributes to its poor performance on unseen objects and/or viewpoints. Second, we propose a novel framework for object tracking inspired by the IC-LK framework, which we refer to as Deep-LK. Finally, we show impressive results demonstrating that Deep-LK substantially outperforms GOTURN and demonstrate comparable tracking performance against current state-of-the-art deep trackers on high frame-rate sequences whilst being an order of magnitude (100 FPS) computationally efficient.

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