@proceedings {3240, title = {Learning to See Physics via Visual De-animation}, year = {2017}, month = {12/2017}, pages = {152{\textendash}163}, abstract = {
We introduce a paradigm for understanding physical scenes without human annotations. At the core of our system is a physical world representation that is first recovered by a perception module and then utilized by physics and graphics
engines. During training, the perception module and the generative models learn by visual de-animation
{\textemdash} interpreting and reconstructing the visual information stream. During testing, the system first recovers the physical world state, and then uses the generative models for reasoning and future prediction.
Even more so than forward simulation, inverting a physics or graphics engine is a computationally hard problem; we overcome this challenge by using a convolutional inversion network. Our system quickly recognizes the physical world
state from appearance and motion cues, and has the flexibility to incorporate both differentiable and non-differentiable physics and graphics engines. We evaluate our system on both synthetic and real datasets involving multiple physical scenes, and demonstrate that our system performs well on both physical state estimation and reasoning problems. We further show that the knowledge learned on the synthetic dataset generalizes to constrained real images.
}, url = {http://papers.nips.cc/paper/6620-learning-to-see-physics-via-visual-de-animation.pdf}, author = {Jiajun Wu and Lu, Erika and Kohli, Pushmeet and William T. Freeman and Joshua B. Tenenbaum}, editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett} } @proceedings {3241, title = {MarrNet: 3D Shape Reconstruction via 2.5D Sketches}, year = {2017}, month = {12/2017}, pages = {540{\textendash}550}, publisher = {Curran Associates, Inc.}, address = {Long Beach, CA}, abstract = {

3D object reconstruction from a single image is a highly under-determined problem, requiring strong prior knowledge of plausible 3D shapes. This introduces challenge for learning-based approaches, as 3D object annotations in real images are scarce. Previous work chose to train on synthetic data with ground truth 3D information, but suffered from the domain adaptation issue when tested on real data. In this work, we propose an end-to-end trainable framework, sequentially estimating 2.5D sketches and 3D object shapes. Our disentangled, two-step formulation has three advantages. First, compared to full 3D shape, 2.5D sketches are much easier to be recovered from a 2D image, and to transfer from synthetic to real data. Second, for 3D reconstruction from the 2.5D sketches, we can easily transfer the learned model on synthetic data to real images, as rendered 2.5D sketches are invariant to object appearance variations in real images, including lighting, texture, etc. This further relieves the domain adaptation problem. Third, we derive differentiable projective functions from 3D shape to 2.5D sketches, making the framework end-to-end trainable on real images, requiring no real-image annotations. Our framework achieves state-of-the-art performance on 3D shape reconstruction.

}, url = {http://papers.nips.cc/paper/6657-marrnet-3d-shape-reconstruction-via-25d-sketches.pdf}, author = {Jiajun Wu and Wang, Yifan and Xue, Tianfan and Sun, Xingyuan and William T. Freeman and Joshua B. Tenenbaum}, editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett} } @proceedings {3242, title = {Shape and Material from Sound}, year = {2017}, month = {12/2017}, pages = {1278{\textendash}1288}, address = {Long Beach, CA}, abstract = {

What can we infer from hearing an object falling onto the ground? Based on knowledge of the physical world, humans are able to infer rich information from such limited data: rough shape of the object, its material, the height of falling, etc. In this paper, we aim to approximate such competency. We first mimic the human knowledge about the physical world using a fast physics-based generative model. Then, we present an analysis-by-synthesis approach to infer properties of the falling object. We further approximate human past experience by directly mapping audio to object properties using deep learning with self-supervision. We evaluate our method through behavioral studies, where we compare human predictions with ours on inferring object shape, material, and initial height of falling. Results show that our method achieves near-human performance, without any annotations.

}, url = {http://papers.nips.cc/paper/6727-shape-and-material-from-sound.pdf}, author = {zhang, zhoutong and Qiujia Li and Zhengjia Huang and Jiajun Wu and Joshua B. Tenenbaum and William T. Freeman}, editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett} }