arXiv subjects

Jing-En Huang

Publications and source records attributed to Jing-En Huang.

4 recordsLinked to original sources

arXiv2026

Every Camera Effect, Every Time, All at Once: 4D Gaussian Ray Tracing for Physics-based Camera Effect Data Generation

Current video diffusion models can generate recognizable camera-effect appearances but do not reliably follow specified physical parameters such as field of view, aperture, and readout time, partly because real dynamic videos with diverse and accurate camera-effect annotations are scarce. Physics-based simulation provides exact labels but suffers from a sim-to-real gap. Reconstructing real dynamic scenes offers an alternative, but Dynamic NeRF methods render too slowly, whereas rasterization-based Dynamic Gaussian Splatting cannot model the light transport required by these effects. We propose 4D Gaussian Ray Tracing (4D-GRT), a two-stage data-generation pipeline that reconstructs dynamic scenes as 4D Gaussians from multi-view pinhole videos and re-renders them with physically based camera models under specified parameters. On CamVFX-4D, our controlled benchmark of eight dynamic indoor scenes under four camera effects, 4D-GRT achieves up to 3.4 dB higher PSNR and 6 times faster rendering than MSTH. Finally, we demonstrate two downstream applications: fine-tuning a video generation model on our data to improve camera-effect control and extending our pipeline to monocular input. Project page: https://shigon255.github.io/4DGRT-project-page/.

cs.CV↗

arXiv2026

QVR3D: Query-Guided Visual Reasoning for Training-Free 3D Grounding

3D visual grounding localizes objects in 3D scenes from natural language descriptions, requiring reasoning over semantics, spatial relationships, and multi-view observations. Training-free approaches avoid costly annotations but often rely on explicit scene transformation or scene-level frame selection, which may waste limited visual capacity on query-irrelevant regions. We observe that effective grounding should prioritize observations that provide evidence for the query rather than uniformly represent the entire scene. Based on this insight, we introduce QVR3D, a query-guided visual reasoning framework that operates on RGB-D observations using off-the-shelf foundation models. QVR3D combines Query-Guided Maximum Coverage (QGMC) for selecting complementary query-relevant views with Grounding Probability Estimation (GPE) that integrates instance confidence, attribute consistency, and relationship consistency. The framework supports both single- and multi-object grounding without task-specific adaptation. Experiments on ScanRefer and Nr3D achieve state-of-the-art zero-shot performance, while QGMC also improves frame efficiency across multiple 3D vision-language tasks. Our code is available at https://github.com/jwliao1209/QVR3D.

cs.CV↗

arXiv2026

An Improved Variational Method for Image Denoising

The total variation (TV) method is an image denoising technique that aims to reduce noise by minimizing the total variation of the image, which measures the variation in pixel intensities. The TV method has been widely applied in image processing and computer vision for its ability to preserve edges and enhance image quality. In this paper, we propose a Mixed-norm TV (MixTV) model for image denoising and the associated numerical algorithm to carry out the procedure, which is particularly effective in removing several types of noise and their combinations. Our MixTV admits a unique solution and the associated numerical algorithm guarantees convergence. Numerical experiments are demonstrated to show improved effectiveness and denoising quality compared to other TV models. Such encouraging results further enhance the utility of the TV method in image processing. Our project page is available at https://jing-en-huang.github.io/MixTV.

cs.CV↗

arXiv2026

Gen-n-Val: Agentic Image Data Generation and Validation

The data scarcity, label noise, and long-tailed category imbalance remain important and unresolved challenges in many computer vision tasks, such as object detection and instance segmentation, especially on large-vocabulary benchmarks like LVIS, where most categories appear in only a few images. Current synthetic data generation methods still suffer from multiple objects per mask, inaccurate segmentation, incorrect category labels, and other issues, limiting their effectiveness. To address these issues, we introduce Gen-n-Val, a novel agentic data generation framework that leverages Layer Diffusion (LD), a Large Language Model (LLM), and a Vision Large Language Model (VLLM) to produce high-quality and diverse instance masks and images for object detection and instance segmentation. Gen-n-Val consists of two agents: (1) the LD prompt agent, an LLM, optimizes rompts to encourage LD to generate high-quality foreground single-object images and corresponding segmentation masks; and (2) the data validation agent, a VLLM, filters out low-quality synthetic instance images. The system prompts for both agents are optimized by TextGrad. Compared to state-of-the-art synthetic data approaches like MosaicFusion, our approach reduces invalid synthetic data from 50% to 7% and improves performance by 7.6% on rare classes in LVIS instance segmentation with Mask R-CNN, and by 3.6% mAP on rare classes in COCO instance segmentation with YOLOv9c and YOLO11m. Furthermore, Gen-n-Val shows significant improvements (7.1% mAP) over YOLO-Worldv2-M in open-vocabulary object detection benchmarks with YOLO11m. Moreover, Gen-n-Val has scalability in model capacity and dataset size. The code is available at https://github.com/aiiu-lab/Gen-n-Val.

cs.CV↗