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Daniel Vlasic

7 accepted papers

2023

NAVI: Category-Agnostic Image Collections with High-Quality 3D Shape and Pose Annotations

Varun Jampani, Kevis-kokitsi Maninis, Andreas Engelhardt, Arjun Karpur, Karen Truong, Kyle Sargent +10
NeurIPS 2023poster

Recent advances in neural reconstruction enable high-quality 3D object reconstruction from casually captured image collections. Current techniques mostly analyze their progress on relatively simple image collections where SfM techniques can provide ground-truth (GT) camera poses. We note that SfM te…

Cited by 27Source ↗PDF ↗code ↗Scholar ↗
2021

AutoFlow: Learning a Better Training Set for Optical Flow

Deqing Sun, Daniel Vlasic, Charles Herrmann, Varun Jampani, Michael Krainin, Huiwen Chang +3
CVPR 2021poster

Synthetic datasets play a critical role in pre-training CNN models for optical flow, but they are painstaking to generate and hard to adapt to new applications. To automate the process, we present AutoFlow, a simple and effective method to render training data for optical flow that optimizes the per…

Cited by 132PDF ↗code ↗Scholar ↗
2021

Differentiable Surface Rendering via Non-Differentiable Sampling

Forrester Cole, Kyle Genova, Avneesh Sud, Daniel Vlasic, Zhoutong Zhang
ICCV 2021poster

We present a method for differentiable rendering of 3D surfaces that supports both explicit and implicit representations, provides derivatives at occlusion boundaries, and is fast and simple to implement. The method first samples the surface using non-differentiable rasterization, then applies diffe…

Cited by 49PDF ↗Scholar ↗
2021

LASR: Learning Articulated Shape Reconstruction From a Monocular Video

Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Huiwen Chang +3
CVPR 2021poster

Remarkable progress has been made in 3D reconstruction of rigid structures from a video or a collection of images. However, it is still challenging to reconstruct nonrigid structures from RGB inputs, due to the under-constrained nature of this problem. While template-based approaches, such as parame…

Cited by 129PDF ↗code ↗Scholar ↗
2021

ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction

Gengshan Yang, Deqing Sun, Varun Jampani, Daniel Vlasic, Forrester Cole, Ce Liu +1
NeurIPS 2021spotlight

We introduce ViSER, a method for recovering articulated 3D shapes and dense3D trajectories from monocular videos. Previous work on high-quality reconstruction of dynamic 3D shapes typically relies on multiple camera views, strong category-specific priors, or 2D keypoint supervision. We show that no…

Cited by 96Source ↗PDF ↗code ↗Scholar ↗
2019

Learning Shape Templates With Structured Implicit Functions

Kyle Genova, Forrester Cole, Daniel Vlasic, Aaron Sarna, William T. Freeman, Thomas Funkhouser
ICCV 2019poster

Template 3D shapes are useful for many tasks in graphics and vision, including fitting observation data, analyzing shape collections, and transferring shape attributes. Because of the variety of geometry and topology of real-world shapes, previous methods generally use a library of hand-made templat…

Cited by 425PDF ↗Scholar ↗
2018

Unsupervised Training for 3D Morphable Model Regression

Kyle Genova, Forrester Cole, Aaron Maschinot, Aaron Sarna, Daniel Vlasic, William T. Freeman
CVPR 2018poster

We present a method for training a regression network from image pixels to 3D morphable model coordinates using only unlabeled photographs. The training loss is based on features from a facial recognition network, computed on-the-fly by rendering the predicted faces with a differentiable renderer. T…

Cited by 372Source ↗PDF ↗code ↗Scholar ↗