Biography

I am Yi-Ling Qiao (pronunced as “Yee-ling Chee-ow”), a fifth-year Ph.D. student in UMD GAMMA Group advised by Prof. Ming C. Lin at University of Maryland, College Park. During my undergraduate years, I was advised by Prof. Lin Gao and Prof. Xilin Chen.

My research is about physically-based simulation, graphics and machine learning and is supported by Meta PhD Fellowship (AR/VR Computer Graphics Track). Here is my Research Statement.

Software:

Education
  • Ph.D. Student in Computer Science, 2019 - present

    University of Maryland, College Park

  • B.E. in Computer Science, 2015 - 2019

    University of Chinese Academy of Sciences

  • B.S. in Mathematics and Applied Mathematics, 2015 - 2019

    University of Chinese Academy of Sciences

Recent Publications

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HandyPriors - Physically Consistent Perception of Hand-Object Interactions with Differentiable Priors
HandyPriors - Physically Consistent Perception of Hand-Object Interactions with Differentiable Priors

Various heuristic objectives for modeling hand-object interaction have been proposed in past work. However, due to the lack of a cohesive framework, these objectives often possess a narrow scope of applicability and are limited by their efficiency or accuracy. In this paper, we propose HandyPriors, a unified and general pipeline for pose estimation in human-object interaction scenes by leveraging recent advances in differentiable physics and rendering. Our approach employs rendering priors to align with input images and segmentation masks along with physics priors to mitigate penetration and relative-sliding across frames. Furthermore, we present two alternatives for hand and object pose estimation. The optimization-based pose estimation achieves higher accuracy, while the filtering-based tracking, which utilizes the differentiable priors as dynamics and observation models, executes faster. We demonstrate that HandyPriors attains comparable or superior results in the pose estimation task, and that the differentiable physics module can predict contact information for pose refinement. We also show that our approach generalizes to perception tasks, including robotic hand manipulation and human-object pose estimation in the wild.

Towards Generalist Robots - Learning Paradigms for Scalable Skill Acquisition
Towards Generalist Robots - Learning Paradigms for Scalable Skill Acquisition

We have witnessed very impressive progress in large-scale and multi-modal foundation/generative models in recent months. We believe making use of such models in a reasonable way could really enable robots to acquire diverse skills. In the recent white paper, we discussed how we can automate the whole pipeline for robotic skill learning, from low-level asset generation, texture generation, to high-level scene, task and reward generation. Once we obtain such a diverse suite of tasks and environments, we can offload policy training to RL of trajectory optimization to solve all the generated low-level tasks, and finally distill all the learned closed-loop policy into a unified policy model. Apart from scaling up in simulation, how to use real-world data more effectively is another promising research direction. Real-world human demonstrations can be found at scale, but typically only provides spatial trajectory information and doesn’t advise how to recover from error compounding during policy rollout. Motivated by these observations and thoughts, this workshop seeks to discuss and compare the advantages and limitations of different paradigms for scaling up skill learning - scaling up simulation, leveraging generative models, exploiting unstructured passive human demonstration, scaling up structured demonstration collection in the real world, etc.

Dynamic Mesh-Aware Radiance Fields
Dynamic Mesh-Aware Radiance Fields

Embedding polygonal mesh assets within photorealistic Neural Radience Fields (NeRF) volumes, such that they can be rendered and their dynamics simulated in a physically consistent manner with the NeRF, is under-explored from the system perspective of integrating NeRF into the traditional graphics pipeline. This paper designs a two-way coupling between mesh and NeRF during rendering and simulation. We first review the light transport equations for both mesh and NeRF, then distill them into a straightforward algorithm for updating radiance and throughput along a cast ray with an arbitrary number of bounces. To resolve the discrepancy between the linear color space that the path tracer assumes, versus the sRGB color space that standard NeRF uses, we train NeRF with High Dynamic Range (HDR) images. We also present a strategy to estimate light sources and cast shadows on the NeRF. Finally, we consider how the hybrid surface-volumetric formulation can be efficiently integrated with a high-performance physics simulator that supports cloth, rigid and soft bodies. The full rendering and simulation system can be run on a GPU at interactive rates. We show that a hybrid system approach outperforms alternatives in visual realism for mesh insertion, because it allows realistic light transport from volumetric NeRF media onto surfaces, which affects the appearance of reflective/refractive surfaces and illumination of diffuse surfaces informed by the scene.

PAC-NeRF - Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification
PAC-NeRF - Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification

Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. This precludes their applicability in a vast majority of scenes where object geometries are complex or unknown. In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology. To this end, we propose “Physics Augmented Continuum Neural Radiance Fields” (PAC-NeRF), to estimate both the unknown geometry and physical parameters of highly dynamic objects from multi-view videos. We design PAC-NeRF to only ever produce physically plausible states by enforcing the neural radiance field to follow the conservation laws of continuum mechanics. For this, we design a hybrid Eulerian-Lagrangian representation of the neural radiance field, i.e., we use the Eulerian grid representation for NeRF density and color fields, while advecting the neural radiance fields via Lagrangian particles. This hybrid Eulerian-Lagrangian representation seamlessly blends efficient neural rendering with the material point method (MPM) for robust differentiable physics simulation. We validate the effectiveness of our proposed framework on geometry and physical parameter estimation over a vast range of materials, including elastic bodies, plasticine, sand, Newtonian and non-Newtonian fluids, and demonstrate significant performance gain on most tasks.

NeuPhysics - Editable Neural Geometry and Physics from Monocular Videos
NeuPhysics - Editable Neural Geometry and Physics from Monocular Videos

We present a method for learning geometry and physics parameters of a dynamic scene requiring only a monocular RGB video. Our approach uses a hybrid representation of neural fields and hexahedra mesh, enabling objects in the scene to be interactively edited, and synthesized from novel views. To decouple the learning of underlying scene geometry from dynamic motion, we learn a time-invariant signed distance function which serves as a reference frame, as well as an associated deformation field that is conditioned on each time step. We design a two-way conversion between the neural field and corresponding mesh representation, which allows us to bridge the neural representation with a differentiable physics simulator, and therefore estimate physics parameters from the source video, by minimizing a cycle consistency loss. This flexible, hybrid representation also allows a user to easily edit 3D objects from the source video by directly editing the recovered hexahedra mesh, and propagating this operation back to the neural field. In Experiments, our method achieves higher-quality mesh and video reconstruction of dynamic scenes compared to other competitive Neural Field methods. Finally, we provide extensive examples which demonstrate our method’s ability to extract useful 3D representations of dynamic scenes from videos captured with consumer-grade cameras.

Learning-based Intrinsic Reflectional Symmetry Detection
Learning-based Intrinsic Reflectional Symmetry Detection

Reflectional symmetry is a ubiquitous pattern in nature. Previous works usually solve this problem by voting or sampling, suffering from high computational cost and randomness. In this paper, we propose a learning-based approach to intrinsic reflectional symmetry detection. Instead of directly finding symmetric point pairs, we parametrize this self-isometry using a functional map matrix, which can be easily computed given the signs of Laplacian eigenfunctions under the symmetric mapping. Therefore, we manually label the eigenfunction signs for a variety of shapes and train a novel neural network to predict the sign of each eigenfunction under symmetry. Our network aims at learning the global property of functions and consequently converts the problem defined on the manifold to the functional domain. By disentangling the prediction of the matrix into separated bases, our method generalizes well to new shapes and is invariant under perturbation of eigenfunctions. Through extensive experiments, we demonstrate the robustness of our method in challenging cases, including different topology and incomplete shapes with holes. By avoiding random sampling, our learning-based algorithm is over 20 times faster than state-of-the-art methods, and meanwhile, is more robust, achieving higher correspondence accuracy in commonly used metrics.

Learning on 3D Meshes with Laplacian Encoding and Pooling
Learning on 3D Meshes with Laplacian Encoding and Pooling

3D models are commonly used in computer vision and graphics. With the wider availability of mesh data, an efficient and intrinsic deep learning approach to processing 3D meshes is in great need. Unlike images, 3D meshes have irregular connectivity, requiring careful design to capture relations in the data. To utilize the topology information while staying robust under different triangulations, we propose to encode mesh connectivity using Laplacian spectral analysis, along with mesh feature aggregation blocks (MFABs) that can split the surface domain into local pooling patches and aggregate global information amongst them. We build a mesh hierarchy from fine to coarse using Laplacian spectral clustering, which is flexible under isometric transformations. Inside the MFABs there are pooling layers to collect local information and multi-layer perceptrons to compute vertex features of increasing complexity. To obtain the relationships among different clusters, we introduce a Correlation Net to compute a correlation matrix, which can aggregate the features globally by matrix multiplication with cluster features. Our network architecture is flexible enough to be used on meshes with different numbers of vertices. We conduct several experiments including shape segmentation and classification, and our method outperforms state-of-the-art algorithms for these tasks on the ShapeNet and COSEG datasets.

Automatic Unpaired Shape Deformation Transfer
Automatic Unpaired Shape Deformation Transfer

Transferring deformation from a source shape to a target shape is a very useful technique in computer graphics. State-of-the-art deformation transfer methods require either point-wise correspondences between source and target shapes, or pairs of deformed source and target shapes with corresponding deformations. However, in most cases, such correspondences are not available and cannot be reliably established using an automatic algorithm. Therefore, substantial user effort is needed to label the correspondences or to obtain and specify such shape sets. In this work, we propose a novel approach to automatic deformation transfer between two unpaired shape sets without correspondences. 3D deformation is represented in a highdimensional space. To obtain a more compact and effective representation, two convolutional variational autoencoders are learned to encode source and target shapes to their latent spaces. We exploit a Generative Adversarial Network (GAN) to map deformed source shapes to deformed target shapes, both in the latent spaces, which ensures the obtained shapes from the mapping are indistinguishable from the target shapes. This is still an under-constrained problem, so we further utilize a reverse mapping from target shapes to source shapes and incorporate cycle consistency loss, i.e. applying both mappings should reverse to the input shape. This VAE-Cycle GAN (VC-GAN) architecture is used to build a reliable mapping between shape spaces. Finally, a similarity constraint is employed to ensure the mapping is consistent with visual similarity, achieved by learning a similarity neural network that takes the embedding vectors from the source and target latent spaces and predicts the light field distance between the corresponding shapes. Experimental results show that our fully automatic method is able to obtain high-quality deformation transfer results with unpaired data sets, comparable or better than existing methods where strict correspondences are required.

SF-Net Learning Scene Flow from RGB-D Images with CNNs
SF-Net Learning Scene Flow from RGB-D Images with CNNs

With the rapid development of depth sensors, RGB-D data has become much more accessible. Scene flow is one of the fundamental ways to understand the dynamic content in RGB-D image sequences. Traditional approaches estimate scene flow using registration and smoothness or local rigidity priors, which is slow and prone to errors when the priors are not fully satisfied. To address such challenges, learning based methods provide an attractive alternative. However, trivially applying CNN-based optical flow estimation methods does not produce satisfactory results. How to use deep learning to improve the estimation of scene flow from RGB-D images remains unexplored. In this work, we propose a novel learning based framework to estimate scene flow, which takes both brightness and scene flow losses. Given a pair of RGB-D images, the brightness loss is used to measure the disparity between the first RGB-D image and the deformed second RGB-D image using the scene flow, and the scene flow loss is used to learn from the ground truth of scene flow. We build a convolutional neural network to simultaneously optimize both losses. Extensive experiments on both synthetic and real-world datasets show that our method is significantly faster than existing methods and outperforms stateof-the-art real-time methods in accuracy.