142,018 research outputs found
Synthesizing Diverse Human Motions in 3D Indoor Scenes
We present a novel method for populating 3D indoor scenes with virtual humans
that can navigate the environment and interact with objects in a realistic
manner. Existing approaches rely on high-quality training sequences that
capture a diverse range of human motions in 3D scenes. However, such motion
data is costly, difficult to obtain and can never cover the full range of
plausible human-scene interactions in complex indoor environments. To address
these challenges, we propose a reinforcement learning-based approach to learn
policy networks that predict latent variables of a powerful generative motion
model that is trained on a large-scale motion capture dataset (AMASS). For
navigating in a 3D environment, we propose a scene-aware policy training scheme
with a novel collision avoidance reward function. Combined with the powerful
generative motion model, we can synthesize highly diverse human motions
navigating 3D indoor scenes, meanwhile effectively avoiding obstacles. For
detailed human-object interactions, we carefully curate interaction-aware
reward functions by leveraging a marker-based body representation and the
signed distance field (SDF) representation of the 3D scene. With a number of
important training design schemes, our method can synthesize realistic and
diverse human-object interactions (e.g.,~sitting on a chair and then getting
up) even for out-of-distribution test scenarios with different object shapes,
orientations, starting body positions, and poses. Experimental results
demonstrate that our approach outperforms state-of-the-art human-scene
interaction synthesis frameworks in terms of both motion naturalness and
diversity. Video results are available on the project page:
https://zkf1997.github.io/DIMOS
Unsupervised Discovery of Parts, Structure, and Dynamics
Humans easily recognize object parts and their hierarchical structure by
watching how they move; they can then predict how each part moves in the
future. In this paper, we propose a novel formulation that simultaneously
learns a hierarchical, disentangled object representation and a dynamics model
for object parts from unlabeled videos. Our Parts, Structure, and Dynamics
(PSD) model learns to, first, recognize the object parts via a layered image
representation; second, predict hierarchy via a structural descriptor that
composes low-level concepts into a hierarchical structure; and third, model the
system dynamics by predicting the future. Experiments on multiple real and
synthetic datasets demonstrate that our PSD model works well on all three
tasks: segmenting object parts, building their hierarchical structure, and
capturing their motion distributions.Comment: ICLR 2019. The first two authors contributed equally to this wor
Single View Reconstruction for Human Face and Motion with Priors
Single view reconstruction is fundamentally an under-constrained problem. We aim to develop new approaches to model human face and motion with model priors that restrict the space of possible solutions. First, we develop a novel approach to recover the 3D shape from a single view image under challenging conditions, such as large variations in illumination and pose. The problem is addressed by employing the techniques of non-linear manifold embedding and alignment. Specifically, the local image models for each patch of facial images and the local surface models for each patch of 3D shape are learned using a non-linear dimensionality reduction technique, and the correspondences between these local models are then learned by a manifold alignment method. Local models successfully remove the dependency of large training databases for human face modeling. By combining the local shapes, the global shape of a face can be reconstructed directly from a single linear system of equations via least square.
Unfortunately, this learning-based approach cannot be successfully applied to the problem of human motion modeling due to the internal and external variations in single view video-based marker-less motion capture. Therefore, we introduce a new model-based approach for capturing human motion using a stream of depth images from a single depth sensor. While a depth sensor provides metric 3D information, using a single sensor, instead of a camera array, results in a view-dependent and incomplete measurement of object motion. We develop a novel two-stage template fitting algorithm that is invariant to subject size and view-point variations, and robust to occlusions. Starting from a known pose, our algorithm first estimates a body configuration through temporal registration, which is used to search the template motion database for a best match. The best match body configuration as well as its corresponding surface mesh model are deformed to fit the input depth map, filling in the part that is occluded from the input and compensating for differences in pose and body-size between the input image and the template. Our approach does not require any makers, user-interaction, or appearance-based tracking.
Experiments show that our approaches can achieve good modeling results for human face and motion, and are capable of dealing with variety of challenges in single view reconstruction, e.g., occlusion
VIBE: Video Inference for Human Body Pose and Shape Estimation
Human motion is fundamental to understanding behavior. Despite progress on
single-image 3D pose and shape estimation, existing video-based
state-of-the-art methods fail to produce accurate and natural motion sequences
due to a lack of ground-truth 3D motion data for training. To address this
problem, we propose Video Inference for Body Pose and Shape Estimation (VIBE),
which makes use of an existing large-scale motion capture dataset (AMASS)
together with unpaired, in-the-wild, 2D keypoint annotations. Our key novelty
is an adversarial learning framework that leverages AMASS to discriminate
between real human motions and those produced by our temporal pose and shape
regression networks. We define a temporal network architecture and show that
adversarial training, at the sequence level, produces kinematically plausible
motion sequences without in-the-wild ground-truth 3D labels. We perform
extensive experimentation to analyze the importance of motion and demonstrate
the effectiveness of VIBE on challenging 3D pose estimation datasets, achieving
state-of-the-art performance. Code and pretrained models are available at
https://github.com/mkocabas/VIBE.Comment: CVPR-2020 camera ready. Code is available at
https://github.com/mkocabas/VIB
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