← Search

Junliang Ye

7 accepted papers

2026

Nano3D: A Training-Free Approach for Efficient 3D Editing Without Masks

ICLR 2026poster

3D object editing is essential for interactive content creation in gaming, animation, and robotics, yet current approaches remain inefficient, inconsistent, and often fail to preserve unedited regions. Most methods rely on editing multi-view renderings followed by reconstruction, which introduces ar…

Cited by 0SourcecodeScholar
2026

Part-X-MLLM: Part-aware 3D Multimodal Large Language Model

ICLR 2026poster

We introduce Part-X-MLLM, a native 3D multimodal large language model that unifies diverse 3D tasks by formulating them as programs in a structured, executable grammar. Given an RGB point cloud and a natural language prompt, our model autoregressively generates a single, coherent token sequence enco…

Cited by 4SourcecodeScholar
2026

PhysForge: Generating Physics-Grounded 3D Assets for Interactive Virtual World

ICML 2026poster

Synthesizing physics-grounded 3D assets is a critical bottleneck for interactive virtual worlds and embodied AI. Existing methods predominantly focus on static geometry, overlooking the functional properties essential for interaction. We propose that interactive asset generation must be rooted in fu…

Cited by 0SourceScholar
2025

DeepMesh: Auto-Regressive Artist-mesh Creation with Reinforcement Learning

ICCV 2025poster

Triangle meshes play a crucial role in 3D applications for efficient manipulation and rendering. While auto-regressive methods generate structured meshes by predicting discrete vertex tokens, they are often constrained by limited face counts and mesh incompleteness. To address these challenges, we p…

Cited by 0SourcePDFScholar
2025

ShapeLLM-Omni: A Native Multimodal LLM for 3D Generation and Understanding

NeurIPS 2025spotlight

Recently, the powerful text-to-image capabilities of GPT-4o have led to growing appreciation for native multimodal large language models. However, its multimodal capabilities remain confined to images and text. Yet beyond images, the ability to understand and generate 3D content is equally crucial.…

Cited by 0SourcecodeScholar
2024

AnimatableDreamer: Text-Guided Non-rigid 3D Model Generation and Reconstruction with Canonical Score Distillation

ECCV 2024poster

"Advances in 3D generation have facilitated sequential 3D model generation (a.k.a 4D generation), yet its application for animatable objects with large motion remains scarce. Our work proposes AnimatableDreamer, a text-to-4D generation framework capable of generating diverse categories of non-rigid…

2024

DreamReward: Aligning Human Preference in Text-to-3D Generation

ECCV 2024poster

"3D content creation from text prompts has shown remarkable success recently. However, current text-to-3D methods often generate 3D results that do not align well with human preferences. In this paper, we present a comprehensive framework, coined DreamReward, to learn and improve text-to-3D models f…