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Yinda Zhang

Yinda Zhang

I am a Researsh Scientist at Google. My research interests are mostly around computer vision and computer graphics. Recently, I focus on empowering 3D vision and percetion via machine learning, including dense depth estimation, 3D shape analysis, and 3D scene understanding. I received my Ph.D. in Computer Science from Princeton University, advised by Professor Thomas Funkhouser. Before that, I received a Bachelor degree from Dept. Automation in Tsinghua University, and a Master degree from Dept. ECE in National University of Singapore co-supervised by Prof. Ping Tan and Prof. Shuicheng Yan. Please check my personal webpage for more.
Authored Publications
Google Publications
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    Experiencing InstructPipe: Building Multi-modal AI Pipelines via Prompting LLMs and Visual Programming
    Zhongyi Zhou
    Jing Jin
    Xiuxiu Yuan
    Jun Jiang
    Jingtao Zhou
    Yiyi Huang
    Kristen Wright
    Jason Mayes
    Mark Sherwood
    Ram Iyengar
    Na Li
    Extended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems, ACM, pp. 5 (to appear)
    Preview abstract Foundational multi-modal models have democratized AI access, yet the construction of complex, customizable machine learning pipelines by novice users remains a grand challenge. This paper demonstrates a visual programming system that allows novices to rapidly prototype multimodal AI pipelines. We first conducted a formative study with 58 contributors and collected 236 proposals of multimodal AI pipelines that served various practical needs. We then distilled our findings into a design matrix of primitive nodes for prototyping multimodal AI visual programming pipelines, and implemented a system with 65 nodes. To support users' rapid prototyping experience, we built InstructPipe, an AI assistant based on large language models (LLMs) that allows users to generate a pipeline by writing text-based instructions. We believe InstructPipe enhances novice users onboarding experience of visual programming and the controllability of LLMs by offering non-experts a platform to easily update the generation. View details
    ChatDirector: Enhancing Video Conferencing with Space-Aware Scene Rendering and Speech-Driven Layout Transition
    Brian Moreno Collins
    Karthik Ramani
    Proceedings of the 2024 CHI Conference on Human Factors in Computing Systems, ACM, pp. 16 (to appear)
    Preview abstract Remote video conferencing systems (RVCS) are widely adopted in personal and professional communication. However, they often lack the co-presence experience of in-person meetings. This is largely due to the absence of intuitive visual cues and clear spatial relationships among remote participants, which can lead to speech interruptions and loss of attention. This paper presents ChatDirector, a novel RVCS that overcomes these limitations by incorporating space-aware visual presence and speech-aware attention transition assistance. ChatDirector employs a real-time pipeline that converts participants' RGB video streams into 3D portrait avatars and renders them in a virtual 3D scene. We also contribute a decision tree algorithm that directs the avatar layouts and behaviors based on participants' speech states. We report on results from a user study (N=16) where we evaluated ChatDirector. The satisfactory algorithm performance and complimentary subject user feedback imply that ChatDirector significantly enhances communication efficacy and user engagement. View details
    Learning Personalized High Quality Volumetric Head Avatars from Monocular RGB Videos
    Ziqian Bai
    Danhang "Danny" Tang
    Di Qiu
    Abhimitra Meka
    Mingsong Dou
    Ping Tan
    Thabo Beeler
    2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE
    Preview abstract We propose a method to learn a high-quality implicit 3D head avatar from a monocular RGB video captured in the wild. The learnt avatar is driven by a parametric face model to achieve user-controlled facial expressions and head poses. Our hybrid pipeline combines the geometry prior and dynamic tracking of a 3DMM with a neural radiance field to achieve fine-grained control and photorealism. To reduce over-smoothing and improve out-of-model expressions synthesis, we propose to predict local features anchored on the 3DMM geometry. These learnt features are driven by 3DMM deformation and interpolated in 3D space to yield the volumetric radiance at a designated query point. We further show that using a Convolutional Neural Network in the UV space is critical in incorporating spatial context and producing representative local features. Extensive experiments show that we are able to reconstruct high-quality avatars, with more accurate expression-dependent details, good generalization to out-of-training expressions, and quantitatively superior renderings compared to other state-of-the-art approaches. View details
    Spectral Graphormer: Spectral Graph-based Transformer for Egocentric Two-Hand Reconstruction using Multi-View Color Images
    Danhang "Danny" Tang
    Franziska Müller
    Jonathan Taylor
    Mingsong Dou
    Sasa Petrovic
    Thabo Beeler
    Tze Ho Elden Tse
    Zhengyang Shen
    Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2023), pp. 14666-14677
    Preview abstract We propose a novel transformer-based framework that reconstructs two high fidelity hands from multi-view RGB images. Unlike existing hand pose estimation methods, where one typically trains a deep network to regress hand model parameters from single RGB image, we consider a more challenging problem setting where we directly regress the absolute root poses of two-hands with extended forearm at high resolution from egocentric view. As existing datasets are either infeasible for egocentric viewpoints or lack background variations, we create a large-scale synthetic dataset with diverse scenarios and collect a real dataset from multi-calibrated camera setup to verify our proposed multi-view image feature fusion strategy. To make the reconstruction physically plausible, we propose two strategies: (i) a coarse-to-fine spectral graph convolution decoder to smoothen the meshes during upsampling and (ii) an optimisation-based refinement stage at inference to prevent self-penetrations. Through extensive quantitative and qualitative evaluations, we show that our framework is able to produce realistic two-hand reconstructions and demonstrate the generalisation of synthetic-trained models to real data, as well as real-time AR/VR applications. View details
    Rapsai: Accelerating Machine Learning Prototyping of Multimedia Applications through Visual Programming
    Na Li
    Jing Jin
    Michelle Carney
    Scott Joseph Miles
    Maria Kleiner
    Xiuxiu Yuan
    Anuva Kulkarni
    Xingyu “Bruce” Liu
    Ahmed K Sabie
    Ping Yu
    Ram Iyengar
    Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI), ACM
    Preview abstract In recent years, there has been a proliferation of multimedia applications that leverage machine learning (ML) for interactive experiences. Prototyping ML-based applications is, however, still challenging, given complex workflows that are not ideal for design and experimentation. To better understand these challenges, we conducted a formative study with seven ML practitioners to gather insights about common ML evaluation workflows. This study helped us derive six design goals, which informed Rapsai, a visual programming platform for rapid and iterative development of end-to-end ML-based multimedia applications. Rapsai is based on a node-graph editor to facilitate interactive characterization and visualization of ML model performance. Rapsai streamlines end-to-end prototyping with interactive data augmentation and model comparison capabilities in its no-coding environment. Our evaluation of Rapsai in four real-world case studies (N=15) suggests that practitioners can accelerate their workflow, make more informed decisions, analyze strengths and weaknesses, and holistically evaluate model behavior with real-world input. View details
    InstructPipe: Building Visual Programming Pipelines with Human Instructions
    Zhongyi Zhou
    Jing Jin
    Xiuxiu Yuan
    Jun Jiang
    Jingtao Zhou
    Yiyi Huang
    Kristen Wright
    Jason Mayes
    Mark Sherwood
    Ram Iyengar
    Na Li
    arXiv, vol. 2312.09672 (2023)
    Preview abstract Visual programming provides beginner-level programmers with a coding-free experience to build their customized pipelines. Existing systems require users to build a pipeline entirely from scratch, implying that novice users need to set up and link appropriate nodes all by themselves, starting from a blank workspace. We present InstructPipe, an AI assistant that enables users to start prototyping machine learning (ML) pipelines with text instructions. We designed two LLM modules and a code interpreter to execute our solution. LLM modules generate pseudocode of a target pipeline, and the interpreter renders a pipeline in the node-graph editor for further human-AI collaboration. Technical evaluations reveal that InstructPipe reduces user interactions by 81.1% compared to traditional methods. Our user study (N=16) showed that InstructPipe empowers novice users to streamline their workflow in creating desired ML pipelines, reduce their learning curve, and spark innovative ideas with open-ended commands. View details
    PRIF: Primary Ray-based Implicit Function
    Danhang "Danny" Tang
    Amitabh Varshney
    Brandon Yushan Feng
    European Conference on Computer Vision (ECCV) (2022)
    Preview abstract We introduce a new implicit shape representation called Primary Ray-based Implicit Function (PRIF). In contrast to most existing approaches based on the signed distance function (SDF) which handles spatial locations, our representation operates on oriented rays. Specifically, PRIF is formulated to directly produce the surface hit point of a given input ray, without the expensive sphere-tracing operations, hence enabling efficient shape extraction and differentiable rendering. We demonstrate that neural networks trained to encode PRIF achieve successes in various tasks including single shape representation, category-wise shape generation, shape completion from sparse or noisy observations, inverse rendering for camera pose estimation, and neural rendering with color. View details
    OmniSyn: Synthesizing 360 Videos with Wide-baseline Panoramas
    David Li
    Christian Haene
    Danhang "Danny" Tang
    Amitabh Varshney
    2022 IEEE Conference on Virtual Reality and 3D User Interfaces (VR), IEEE
    Preview abstract Immersive maps such as Google Street View and Bing Streetside provide true-to-life views with a massive collection of panoramas. However, these panoramas are only available at sparse intervals along the path they are taken, resulting in visual discontinuities during navigation. Prior art in view synthesis is usually built upon a set of perspective images, a pair of stereoscopic images, or a monocular image, but barely examines wide-baseline panoramas, which are widely adopted in commercial platforms to optimize bandwidth and storage usage. In this paper, we leverage the unique characteristics of wide-baseline panoramas and present OmniSyn, a novel pipeline for 360° view synthesis between wide-baseline panoramas. OmniSyn predicts omnidirectional depth maps using a spherical cost volume and a monocular skip connection, renders meshes in 360° images, and synthesizes intermediate views with a fusion network. We demonstrate the effectiveness of OmniSyn via comprehensive experimental results including comparison with the state-of-the-art methods on CARLA and Matterport datasets, ablation studies, and generalization studies on street views. We envision our work may inspire future research for this unheeded real-world task and eventually produce a smoother experience for navigating immersive maps. View details
    Opportunistic Interfaces for Augmented Reality: Transforming Everyday Objects into Tangible 6DoF Interfaces Using Ad hoc UI
    Mathieu Le Goc
    Shengzhi Wu
    Danhang "Danny" Tang
    Jun Zhang
    David Joseph New Tan
    Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems, ACM
    Preview abstract Real-time environmental tracking has become a fundamental capability in modern mobile phones and AR/VR devices. However, it only allows user interfaces to be anchored at a static location. Although fiducial and natural-feature tracking overlays interfaces with specific visual features, they typically require developers to define the pattern before deployment. In this paper, we introduce opportunistic interfaces to grant users complete freedom to summon virtual interfaces on everyday objects via voice commands or tapping gestures. We present the workflow and technical details of Ad hoc UI (AhUI), a prototyping toolkit to empower users to turn everyday objects into opportunistic interfaces on the fly. We showcase a set of demos with real-time tracking, voice activation, 6DoF interactions, and mid-air gestures and prospect the future of opportunistic interfaces. View details
    Multiresolution Deep Implicit Functions for 3D Shape Representation
    Zhang Chen
    Kyle Genova
    Sofien Bouaziz
    Christian Haene
    Cem Keskin
    Danhang "Danny" Tang
    ICCV (2021)
    Preview abstract We introduce Multiresolution Deep Implicit Functions (MDIF), a hierarchical representation that can recover fine details, while being able to perform more global operations such as shape completion. Our model represents a complex 3D shape with a hierarchy of latent grids, which can be decoded into different resolutions. Training is performed in an encoder-decoder manner, while the decoder-only optimization is supported during inference, hence can better generalize to novel objects, especially when performing shape completion. To the best of our knowledge, MDIF is the first model that can at the same time (1) reconstruct local detail; (2) perform decoder-only inference; (3) fulfill shape reconstruction and completion. We demonstrate superior performance against prior arts in our experiments. View details
    HumanGPS: Geodesic PreServing Feature for Dense Human Correspondence
    Danhang "Danny" Tang
    Mingsong Dou
    Kaiwen Guo
    Cem Keskin
    Sofien Bouaziz
    Ping Tan
    Computer Vision and Pattern Recognition 2021 (2021), pp. 8
    Preview abstract In this paper, we address the problem of building dense correspondences between human images under arbitrary camera viewpoints and body poses. Prior art either assumes small motion between frames or relies on local descriptors, which cannot handle large motion or visually ambiguous body parts, e.g. left v.s. right hand. In contrast, we propose a deep learning framework that maps each pixel to a feature space, where the feature distances reflect the geodesic distances among pixels as if they were projected onto the surface of a 3D human scan. To this end, we introduce novel loss functions to push features apart according to their geodesic distances on the surface. Without any semantic annotation, the proposed embeddings automatically learn to differentiate visually similar parts and align different subjects into an unified feature space. Extensive experiments show that the learned embeddings can produce accurate correspondences between images with remarkable generalization capabilities on both intra and inter subjects. View details
    Deep Relightable Textures: Volumetric Performance Capture with Neural Rendering
    Abhi Meka
    Christian Haene
    Peter Barnum
    Philip Davidson
    Daniel Erickson
    Jonathan Taylor
    Sofien Bouaziz
    Wan-Chun Alex Ma
    Ryan Overbeck
    Thabo Beeler
    Paul Debevec
    Shahram Izadi
    Christian Theobalt
    Christoph Rhemann
    SIGGRAPH Asia and TOG (2020)
    Preview abstract The increasing demand for 3D content in augmented and virtual reality has motivated the development of volumetric performance capture systems such as the Light Stage. Recent advances are pushing free viewpoint relightable videos of dynamic human performances closer to photorealistic quality. However, despite significant efforts, these sophisticated systems are limited by reconstruction and rendering algorithms which do not fully model complex 3D structures and higher order light transport effects such as global illumination and sub-surface scattering. In this paper, we propose a system that combines traditional geometric pipelines with a neural rendering scheme to generate photorealistic renderings of dynamic performances under desired viewpoint and lighting. Our system leverages deep neural networks that model the classical rendering process to learn implicit features that represent the view-dependent appearance of the subject independent of the geometry layout, allowing for generalization to unseen subject poses and even novel subject identity. Detailed experiments and comparisons demonstrate the efficacy and versatility of our method to generate high-quality results, significantly outperforming the existing state-of-the-art solutions. View details
    Deep Implicit Volume Compression
    Danhang "Danny" Tang
    Phil Chou
    Christian Haene
    Mingsong Dou
    Jonathan Taylor
    Shahram Izadi
    Sofien Bouaziz
    Cem Keskin
    CVPR (2020)
    Preview abstract We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in voxel grids and their corresponding textures. To compress the TSDF our method relies on a block-based neural architecture trained end-to-end achieving state-of-the-art compression rates. To prevent topological errors we losslessly compress the signs of the TSDF which also as a side effect bounds the maximum reconstruction error by the voxel size. To compress the affiliated texture we designed a fast block-base charting and Morton packing technique generating a coherent image that can be efficiently compressed using existing image-based compression algorithms. We demonstrate the performance of our algorithms on a large set of 4D performance sequences captured using multi-camera RGBD setups. View details
    Preview abstract Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges. We present a novel approach based on neural networks for depth estimation that combines stereo from dual cameras with stereo from a dual-pixel sensor, which is increasingly common on consumer cameras. Our network uses a novel architecture to fuse these two sources of information and can overcome the above-mentioned limitations of pure binocular stereo matching. Our method provides a dense depth map with sharp edges, which is crucial for computational photography applications like synthetic shallow-depth-of-field or 3D Photos. Additionally, we avoid the inherent ambiguity due to the aperture problem in stereo cameras by designing the stereo baseline to be orthogonal to the dual-pixel baseline. We present experiments and comparisons with state-of-the-art approaches to show that our method offers a substantial improvement over previous works. View details
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