Author name cluster

Yishun Dou

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

4 papers

2 author rows

AAAI Conference 2025 Conference Paper

InstantSticker: Realistic Decal Blending via Disentangled Object Reconstruction

Yi Zhang
Xiaoyang Huang
Yishun Dou
Yue Shi
Rui Shi
Ye Chen
Bingbing Ni
Wenjun Zhang

We present InstantSticker, a disentangled reconstruction pipeline based on Image-Based Lighting (IBL), which focuses on highly realistic decal blending, simulates stickers attached to the reconstructed surface, and allows for instant editing and real-time rendering. To achieve stereoscopic impression of the decal, we introduce shadow factor into IBL, which can be adaptively optimized during training. This allows the shadow brightness of surfaces to be accurately decomposed rather than baked into the diffuse color, ensuring that the edited texture exhibits authentic shading. To address the issues of warping and blurriness in previous methods, we apply As-Rigid-As-Possible (ARAP) parameterization to pre-unfold a specified area of the mesh and use the local UV mapping combined with a neural texture map to enhance the ability to express high-frequency details in that area. For instant editing, we utilize the Disney BRDF model, explicitly defining material colors with 3-channel diffuse albedo. This enables instant replacement of albedo RGB values during the editing process, avoiding the prolonged optimization required in previous approaches. In our experiment, we introduce the Ratio Variance Warping (RVW) metric to evaluate the local geometric warping of the decal area. Extensive experimental results demonstrate that our method surpasses previous decal blending methods in terms of editing quality, editing speed and rendering speed, achieving the state-of-the-art.

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AAAI Conference 2025 Conference Paper

Neural Block Compression: Variable Bitrates Feature Blocks for Texture Representation

Rui Shi
Yishun Dou
Zhong Zheng
Xiangzhong Fang
Wenjun Zhang
Bingbing Ni

The imperative for compression of material textures emerges from the critical demand for high-quality rendering, which necessitates sophisticated textures that, in turn, require substantial storage and memory resources. Thus, low-bitrate compression is crucial, especially in modern games demanding higher texture resolutions. Concurrent methodologies in texture compression predominantly employ a block-based paradigm based on color space, which inevitably leads to representational redundancies and a limited compression scope, particularly at lower bitrates. In the context of mobile devices, bandwidth during texture loading and runtime memory are major bottlenecks, making existing compression algorithms inadequate for high-resolution textures. To mitigate these limitations, we propose a novel multi-resolution texture compression scheme, Neural Block Compression (NBC), developed within the neural feature domain. Our encoding scheme is constructed on a hierarchy of multi-resolution neural feature blocks, and the key ingredient is the variable bitrates quantization scheme. It allocates higher bitrates to higher feature mip-levels and lower bitrates to lower feature mip-levels, thereby extending the concept of block compression from color domain into neural feature domain. Extensive experiments demonstrate the superior texture compression quality achieved by the proposed scheme, especially at low bitrates.

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AAAI Conference 2024 Conference Paper

FocalDreamer: Text-Driven 3D Editing via Focal-Fusion Assembly

Yuhan Li
Yishun Dou
Yue Shi
Yu Lei
Xuanhong Chen
Yi Zhang
Peng Zhou
Bingbing Ni

While text-3D editing has made significant strides in leveraging score distillation sampling, emerging approaches still fall short in delivering separable, precise and consistent outcomes that are vital to content creation. In response, we introduce FocalDreamer, a framework that merges base shape with editable parts according to text prompts for fine-grained editing within desired regions. Specifically, equipped with geometry union and dual-path rendering, FocalDreamer assembles independent 3D parts into a complete object, tailored for convenient instance reuse and part-wise control. We propose geometric focal loss and style consistency regularization, which encourage focal fusion and congruent overall appearance. Furthermore, FocalDreamer generates high-fidelity geometry and PBR textures which are compatible with widely-used graphics engines. Extensive experiments have highlighted the superior editing capabilities of FocalDreamer in both quantitative and qualitative evaluations.

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ECAI Conference 2020 Conference Paper

Belief Map Enhancement Network for Accurate Human Pose Estimation

Jie Liu 0040
Yishun Dou
Wenjie Zhang 0006
Jie Tang 0006
Gangshan Wu

It is a common practice for pose estimation models to output fixed-size low-resolution belief maps for the body keypoints. The coordinates of the highest belief location are then extracted for each of the body keypoints. When mapping this coarse-grained coordinates back into the fine-grained input space, a minor deviation from the ground-truth location will be magnified many times. So, we can usually get more accurate estimation by using larger belief maps. However, the problem is that we can not use too large belief maps due to the limited computational resources. To alleviate this problem, we propose the Belief Map Enhancement Network (EnhanceNet) for more accurate human pose estimation. EnhanceNet enlarges the belief maps by using the efficient sub-pixel operations, which not only increases the belief map resolution but also corrects some wrong predictions at the same time. Our EnhanceNet is simple yet effective. Extensive experiments are conducted on MPII and COCO datasets to verify the effectiveness of our proposed network. Specifically, we achieve consistently improvements on MPII dataset and COCO human pose dataset by applying our EnhanceNet to the state-of-the-art methods. Our EnhanceNet can be easily inserted into existing networks.

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