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Register a new userHuman Motion Transfer with 3D Constraints and Detail Enhancement
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Yang-tian Sun
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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We propose a new method for realistic human motion transfer using a generative adversarial network (GAN), which generates a motion video of a target character imitating actions of a source character, while maintaining high authenticity of the generated results. We tackle the problem by decoupling and recombining the posture information and appearance information of both the source and target characters. The innovation of our approach lies in the use of the projection of a reconstructed 3D human model as the condition of GAN to better maintain the structural integrity of transfer results in different poses. We further introduce a detail enhancement net to enhance the details of transfer results by exploiting the details in real source frames. Extensive experiments show that our approach yields better results both qualitatively and quantitatively than the state-of-the-art methods.
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Human pose transfer (HPT) is an emerging research topic with huge potential in fashion design, media production, online advertising and virtual reality. For these applications, the visual realism of fine-grained appearance details is crucial for production quality and user engagement. However, existing HPT methods often suffer from three fundamental issues: detail deficiency, content ambiguity and style inconsistency, which severely degrade the visual quality and realism of generated images. Aiming towards real-world applications, we develop a more challenging yet practical HPT setting, termed as Fine-grained Human Pose Transfer (FHPT), with a higher focus on semantic fidelity and detail replenishment. Concretely, we analyze the potential design flaws of existing methods via an illustrative example, and establish the core FHPT methodology by combing the idea of content synthesis and feature transfer together in a mutually-guided fashion. Thereafter, we substantiate the proposed methodology with a Detail Replenishing Network (DRN) and a corresponding coarse-to-fine model training scheme. Moreover, we build up a complete suite of fine-grained evaluation protocols to address the challenges of FHPT in a comprehensive manner, including semantic analysis, structural detection and perceptual quality assessment. Extensive experiments on the DeepFashion benchmark dataset have verified the power of proposed benchmark against start-of-the-art works, with 12%-14% gain on top-10 retrieval recall, 5% higher joint localization accuracy, and near 40% gain on face identity preservation. Moreover, the evaluation results offer further insights to the subject matter, which could inspire many promising future works along this direction.
3D human dance motion is a cooperative and elegant social movement. Unlike regular simple locomotion, it is challenging to synthesize artistic dance motions due to the irregularity, kinematic complexity and diversity. It requires the synthesized dance is realistic, diverse and controllable. In this paper, we propose a novel generative motion model based on temporal convolution and LSTM,TC-LSTM, to synthesize realistic and diverse dance motion. We introduce a unique control signal, dance melody line, to heighten controllability. Hence, our model, and its switch for control signals, promote a variety of applications: random dance synthesis, music-to-dance, user control, and more. Our experiments demonstrate that our model can synthesize artistic dance motion in various dance types. Compared with existing methods, our method achieved start-of-the-art results.
The movements of individuals are fundamental to building and maintaining social connections. This pictorial presents Wanderlust, an experimental three-dimensional data visualization on the universal visitation pattern revealed from large-scale mobile phone tracking data. It explores ways of visualizing recurrent flows and the attractive places they implied. Inspired by the 19th-century art movement Impressionism, we develop a multi-layered effect, an impression, of mountains emerging from consolidated flows, to capture the essence of human journeys and urban spatial structure.
This paper introduces a new generative deep learning network for human motion synthesis and control. Our key idea is to combine recurrent neural networks (RNNs) and adversarial training for human motion modeling. We first describe an efficient method for training a RNNs model from prerecorded motion data. We implement recurrent neural networks with long short-term memory (LSTM) cells because they are capable of handling nonlinear dynamics and long term temporal dependencies present in human motions. Next, we train a refiner network using an adversarial loss, similar to Generative Adversarial Networks (GANs), such that the refined motion sequences are indistinguishable from real motion capture data using a discriminative network. We embed contact information into the generative deep learning model to further improve the performance of our generative model. The resulting model is appealing to motion synthesis and control because it is compact, contact-aware, and can generate an infinite number of naturally looking motions with infinite lengths. Our experiments show that motions generated by our deep learning model are always highly realistic and comparable to high-quality motion capture data. We demonstrate the power and effectiveness of our models by exploring a variety of applications, ranging from random motion synthesis, online/offline motion control, and motion filtering. We show the superiority of our generative model by comparison against baseline models.
A long-standing challenge in scene analysis is the recovery of scene arrangements under moderate to heavy occlusion, directly from monocular video. While the problem remains a subject of active research, concurrent advances have been made in the context of human pose reconstruction from monocular video, including image-space feature point detection and 3D pose recovery. These methods, however, start to fail under moderate to heavy occlusion as the problem becomes severely under-constrained. We approach the problems differently. We observe that people interact similarly in similar scenes. Hence, we exploit the correlation between scene object arrangement and motions performed in that scene in both directions: first, typical motions performed when interacting with objects inform us about possible object arrangements; and second, object arrangements, in turn, constrain the possible motions. We present iMapper, a data-driven method that focuses on identifying human-object interactions, and jointly reasons about objects and human movement over space-time to recover both a plausible scene arrangement and consistent human interactions. We first introduce the notion of characteristic interactions as regions in space-time when an informative human-object interaction happens. This is followed by a novel occlusion-aware matching procedure that searches and aligns such characteristic snapshots from an interaction database to best explain the input monocular video. Through extensive evaluations, both quantitative and qualitative, we demonstrate that iMapper significantly improves performance over both dedicated state-of-the-art scene analysis and 3D human pose recovery approaches, especially under medium to heavy occlusion.
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