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Jiacheng Wu

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4 papers

2 author rows

AAAI Conference 2026 Conference Paper

Deep Inverse Shading: Consistent Albedo and Surface Detail Recovery via Generative Refinement

Jiacheng Wu
Ruiqi Zhang
Jie Chen

Reconstructing human avatars using generative priors is essential for achieving versatile and realistic avatar models. Traditional approaches often rely on volumetric representations guided by generative models, but these methods require extensive volumetric rendering queries, leading to slow training. Alternatively, surface-based representations offer faster optimization through differentiable rasterization, yet they are typically limited by vertex count, restricting mesh resolution and scalability when combined with generative priors. Moreover, integrating generative priors into physically based human avatar modeling remains largely unexplored. To address these challenges, we introduce DIS (Deep Inverse Shading), a unified framework for high-fidelity, relightable avatar reconstruction that incorporates generative priors into a coherent surface representation. DIS centers on a mesh-based model that serves as the target for optimizing both surface and material details. The framework fuses multi-view 2D generative surface normal predictions, rich in detail but often inconsistent, into the central mesh using a normal conversion module. This module converts generative normal outputs into per-triangle surface offsets via differentiable rasterization, enabling the capture of fine geometric details beyond sparse vertex limitations. Additionally, DIS integrates a de-shading module, informed by generative priors, to recover accurate material properties such as albedo. This module refines albedo predictions by removing baked-in shading and back-propagates reconstruction errors to further optimize the mesh geometry. Through this joint optimization of geometry and material appearance, DIS achieves physically consistent, high-quality reconstructions suitable for accurate relighting. Our experiments show that DIS delivers SOTA relighting quality, enhanced rendering efficiency, lower memory consumption, and detailed surface reconstruction.

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IROS Conference 2024 Conference Paper

A Case Study on Visual-Audio-Tactile Cross-Modal Retrieval

Jagoda Wojcik
Jiaqi Jiang
Jiacheng Wu
Shan Luo 0001

Cross-Modal Retrieval (CMR), which retrieves relevant items from one modality (e. g. , audio) given a query in another modality (e. g. , visual), has undergone significant advancements in recent years. This capability is crucial for robots to integrate and interpret information across diverse sensory inputs. However, the retrieval space in existing robotic CMR approaches often consists of only one modality, which limits the performance of the robot. In this paper, we propose a novel CMR model that incorporates three different modalities, i. e. , visual, audio, and tactile, for enhanced multi-modal object retrieval, referred to as VAT-CMR. In this model, multi-modal representations are first fused to provide a holistic view of object features. Then, to mitigate the semantic gaps between representations of different modalities, a dominant modality is selected during the classification training phase to improve the distinctiveness of the representations and enhance the retrieval performance. To evaluate our proposed approach, we conducted a case study and the results demonstrate that our VAT-CMR model surpasses competing approaches. Further, our proposed dominant modality selection significantly enhances cross-retrieval accuracy.

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NeurIPS Conference 2023 Conference Paper

MiliPoint: A Point Cloud Dataset for mmWave Radar

Han Cui
Shu Zhong
Jiacheng Wu
Zichao Shen
Naim Dahnoun
Yiren Zhao

Millimetre-wave (mmWave) radar has emerged as an attractive and cost-effective alternative for human activity sensing compared to traditional camera-based systems. mmWave radars are also non-intrusive, providing better protection for user privacy. However, as a Radio Frequency based technology, mmWave radars rely on capturing reflected signals from objects, making them more prone to noise compared to cameras. This raises an intriguing question for the deep learning community: Can we develop more effective point set-based deep learning methods for such attractive sensors? To answer this question, our work, termed MiliPoint, delves into this idea by providing a large-scale, open dataset for the community to explore how mmWave radars can be utilised for human activity recognition. Moreover, MiliPoint stands out as it is larger in size than existing datasets, has more diverse human actions represented, and encompasses all three key tasks in human activity recognition. We have also established a range of point-based deep neural networks such as DGCNN, PointNet++ and PointTransformer, on MiliPoint, which can serve to set the ground baseline for further development.

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AAAI Conference 2019 Conference Paper

Adding Constraints to Bayesian Inverse Problems

Jiacheng Wu
Jian-Xun Wang
Shawn C. Shadden

Using observation data to estimate unknown parameters in computational models is broadly important. This task is often challenging because solutions are non-unique due to the complexity of the model and limited observation data. However, the parameters or states of the model are often known to satisfy additional constraints beyond the model. Thus, we propose an approach to improve parameter estimation in such inverse problems by incorporating constraints in a Bayesian inference framework. Constraints are imposed by constructing a likelihood function based on fitness of the solution to the constraints. The posterior distribution of the parameters conditioned on (1) the observed data and (2) satisfaction of the constraints is obtained, and the estimate of the parameters is given by the maximum a posteriori estimation or posterior mean. Both equality and inequality constraints can be considered by this framework, and the strictness of the constraints can be controlled by constraint uncertainty denoting a confidence on its correctness. Furthermore, we extend this framework to an approximate Bayesian inference framework in terms of the ensemble Kalman filter method, where the constraint is imposed by re-weighing the ensemble members based on the likelihood function. A synthetic model is presented to demonstrate the effectiveness of the proposed method and in both the exact Bayesian inference and ensemble Kalman filter scenarios, numerical simulations show that imposing constraints using the method presented improves identification of the true parameter solution among multiple local minima.

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