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Jinlu Zhang

7 accepted papers

2026

OpenDance: Multimodal Controllable 3D Dance Generation with Large-scale Internet Data

CVPR 2026

Music-driven 3D dance generation offers significant creative potential, yet practical applications demand versatile and multimodal control. Given the highly dynamic and complex human motion covering various styles and genres, dance generation requires satisfying diverse conditions beyond just music

Cited by 0SourceScholar
2025

InteractAnything: Zero-shot Human Object Interaction Synthesis via LLM Feedback and Object Affordance Parsing

CVPR 2025highlight

Recent advances in 3D human-aware generation have made significant progress. However, existing methods still struggle with generating novel Human Object Interaction (HOI) from text, particularly for open-set objects. We identify three main challenges of this task: precise human-object relation reaso…

Cited by 0SourcePDFScholar
2025

StoryWeaver: A Unified World Model for Knowledge-Enhanced Story Character Customization

AAAI 2025technical

Story visualization has gained increasing attention in artificial intelligence. However, existing methods still struggle with maintaining a balance between character identity preservation and text-semantics alignment, largely due to a lack of detailed semantic modeling of the story scene. To tackle…

2024

Fast Text-to-3D-Aware Face Generation and Manipulation via Direct Cross-modal Mapping and Geometric Regularization

ICML 2024poster

Text-to-3D-aware face (T3D Face) generation and manipulation is an emerging research hot spot in machine learning, which still suffers from low efficiency and poor quality. In this paper, we propose an ***E**nd-to-End **E**fficient and **E**ffective* network for fast and accurate T3D face generation…

2024

Move as You Say Interact as You Can: Language-guided Human Motion Generation with Scene Affordance

CVPR 2024highlight

Despite significant advancements in text-to-motion synthesis generating language-guided human motion within 3D environments poses substantial challenges. These challenges stem primarily from (i) the absence of powerful generative models capable of jointly modeling natural language 3D scenes and huma…

2022

MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video

CVPR 2022poster

Recent transformer-based solutions have been introduced to estimate 3D human pose from 2D keypoint sequence by considering body joints among all frames globally to learn spatio-temporal correlation. We observe that the motions of different joints differ significantly. However, the previous methods c…

Cited by 339PDFcodeScholar