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Sijing Wu

5 accepted papers

2026

MIMIC-Bench: Exploring the User-Like Thinking and Mimicking Capabilities of Multimodal Large Language Models

ICLR 2026poster

The rapid advancement of multimodal large language models (MLLMs) has greatly prompted the video interpretation task, and numerous works have been proposed to explore and benchmark the cognition and basic visual reasoning capabilities of MLLMs. However, practical applications on social media platfo…

Cited by 0SourcecodeScholar
2026

ODI-Bench: Can MLLMs Understand Immersive Omnidirectional Environments?

ICLR 2026poster

Omnidirectional images (ODIs) provide full 360$^{\circ} \times$ 180$^{\circ}$ view which are widely adopted in VR, AR and embodied intelligence applications. While multi-modal large language models (MLLMs) have demonstrated remarkable performance on conventional 2D image and video understanding benc…

Cited by 0SourceScholar
2025

Disentangled Clothed Avatar Generation with Layered Representation

ICCV 2025poster

Clothed avatar generation has wide applications in virtual and augmented reality, filmmaking, and more. While existing methods have made progress in creating animatable digital avatars, generating avatars with disentangled components (e.g., body, hair, and clothes) has long been a challenge. In this…

2024

UniProcessor: A Text-induced Unified Low-level Image Processor

ECCV 2024poster

"Image processing, including image restoration, image enhancement, etc., involves generating a high-quality clean image from a degraded input. Deep learning-based methods have shown superior performance for various image processing tasks in terms of single-task conditions. However, they require to t…

2023

GANHead: Towards Generative Animatable Neural Head Avatars

CVPR 2023poster

To bring digital avatars into people's lives, it is highly demanded to efficiently generate complete, realistic, and animatable head avatars. This task is challenging, and it is difficult for existing methods to satisfy all the requirements at once. To achieve these goals, we propose GANHead (Genera…

Cited by 20SourcePDFScholar