Jingdong Zhang

张靖东

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I am currently a third-year Ph.D. student majoring in Computer Science at Texas A&M University, advised by Prof. Wenping Wang and Prof. Xin Li.

I received my Bachelor of Engineering degree at Fudan University in 2023. I also worked as a research assistant at HKUST CSE with Prof. Dan Xu. Previously, I worked with Prof. Tao Chen and Prof. Jiayuan Fan.

My research interests lie in:

  • Scene parsing and reasoning.
  • Multi-modal/-task collaboration.
  • Scalable foundation models.

News

  • Apr. 2026: MTPano has been accepted to SIGGRAPH 2026, meet y'all in LA!
  • Feb. 2026: UniSER has been accepted to CVPR 2026, meet y'all in Denver!
  • Dec. 2025: Will be joining NVIDIA Research in 2026 summer!
  • Aug. 2025: SPGen has been accepted by SIGGRAPH Asia 2025!
  • May. 2025: Start the research internship in Adobe!
  • Jan. 2025: HiTTs has been accepted by ACM MM 2025!
  • Jan. 2025: BridgeNet has been accepted by TPAMI!
  • Jul. 2024: Invited to serve as a reviewer for Pacific Graphics.
  • May. 2024: Start the research internship in Tencent America!
  • Nov. 2023: Invited to serve as a reviewer for CVPR 2024.
  • Nov. 2022: Invited to serve as a reviewer for CVPR 2023.
  • Mar. 2022: Invited to serve as a reviewer for ECCV 2022.
  • Nov. 2021: Invited to serve as a reviewer for CVPR 2022.

Education

Texas A&M University

Department of Computer Science and Engineering

Ph.D. Student

August 2023 - Present
Fudan University

Intelligent Science and Technology (Excellent Class)

Undergraduate Degree

September 2019 - June 2023

Internship

NVIDIA Research

Research Intern, working on foundational 3D understanding.

Jun 2026 - Sep 2026
Adobe

Research Intern, working on generative soft inpainting.

May 2025 - Aug 2025
Tencent America

Research Intern, working on high-quality 3D asset generation.

May 2024 - Aug 2024

Publications

Imagining in 360 demo

Beyond Thinking: Imagining in 360 for Humanoid Visual Search

Arxiv, 2026 arxiv

Abstract: We propose Imagining in 360, a framework for Humanoid Visual Search (HVS) in immersive 360 environments with: i) Decoupled paradigm: separates intuitive spatial imagination from action planning for more grounded exploration, ii) Probabilistic Imaginator: learns spatial priors and guides the Actor via probabilistic estimations, iii) Scalable data engine: a fully automated pipeline producing 1.92M training samples without manual trajectory labels.

MTPano demo

MTPano: Multi-Task Panoramic Scene Understanding via Label-Free Integration of Dense Prediction Priors

Abstract: We propose a foundational panoramic scene understanding model capabale of predicting multiple dense prediction tasks. We achieve this by i) curating large dataset with auto labeling pipeline by existing perspective prediction models, ii) propose PD-BridgeNet to tackle the multi-task interaction challenges under EPR distortions .

UniSER demo

UniSER: A Foundation Model for Unified Soft Effects Removal

Abstract: We propose a foundational image soft effect removal (SER) model with: i) a large, curated pair-wise dataset with diverse soft effects (e.g. lens flare, haze, shadows, and reflections), ii) fine-grained user control with spatial masks and strength control, iii) generalize on zero-shot unseen effects, iv) add or enhance effects.

SPGen demo

SPGen: Spherical Projection as Consistent and Flexible Representation for Single Image 3D Shape Generation

Abstract: SPGen leverages Spherical Projection (SP) to generate high-quality 3D shapes with i) Consistency: SP maps ensure view-consistent and unambiguous 3D reconstruction, ii) Flexibility: Supports arbitrary topologies, iii) Efficiency: Inherit powerful 2D diffusion priors and enables efficient finetuning.

SolidGS demo

SolidGS: Consolidating Gaussian Surfel Splatting for Sparse-View Surface Reconstruction

Arxiv, 2024 arxiv project

Abstract: We present SolidGS, which reconstructs a consolidated Gaussian field from sparse inputs. Given only three input views, our approach enables high-precision and detailed mesh extraction, and high-quality novel view synthesis, achieved within just three minutes.

HiTTs demo

Multi-Task Label Discovery via Hierarchical Task Tokens for Partially Annotated Dense Predictions

Abstract: This research proposes a new approach to multi-task dense predictions with partially labeled data. We introduce hierarchical task tokens (HiTTs) to capture multi-level representations. The global task tokens conduct cross-task interactions and transfer knowledge from labeled to unlabeled tasks.

BridgeNet demo

BridgeNet: Comprehensive and Effective Feature Interactions via Bridge Feature for Multi-task Dense Predictions

Abstract: This work introduces a novel BridgeNet for multi-task learning on dense predictions. It uses a Bridge Feature Extractor (BFE) to create strong bridge features and a Task Pattern Propagation (TPP) to solve the task-pattern entanglement issue, resulting in task-specific features with higher quality.

TIP demo

Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors

Abstract: We introduce a new approach for cross-domain pedestrian one-stage detectors. The paper identifies a foreground-background misalignment issue in image-level feature alignment, and a novel framework, Background-Focused Distribution Alignment (BFDA) is proposed to address this issue.

Research Experience

  • Jun. 2023 - Present: Ph.D. student, Aggie Graphics Group, Texas A&M University
    Advisor: Prof. Wenping Wang and Prof. Xin Li
  • Feb. 2022 - Present: Research Assistant, HKUST
    Advisor: Prof. Dan Xu
  • Jul. 2021 - Present: Research Assistant, Fudan Embedded Deep Learning and Visual Analysis Lab
    Advisor: Prof. Tao Chen and Prof. Jiayuan Fan

Selected Awards

  • The third prize of outstanding Undergraduate Student Scholarship of Fudan University in 2021-2022.
  • The second prize of outstanding Undergraduate Student Scholarship of Fudan University in 2019-2020.
  • Outstanding Student of Fudan University in 2019-2020.
  • The first prize of Advanced Driving Assistance System (ADAS) National Competition by Dell Corporation, May 2021.

Academic Service & Teaching

Reviewer for:
  • CVPR: 2022 - 2026
  • ECCV: 2022 - 2026
  • ICRA: 2024
  • Pacific Graphics: 2024
Teaching Assistant:
  • CSCE 633: Machine Learning
  • CSCE 489: Special Topics in Computer Science and Engineering
  • CSCE 442: Scientific Programming
  • CSCE 222: Discrete Structures for Computing

Miscellaneous

I love photography and road trips. Intermediate skier.