741 research outputs found
Improving Pareto Front Learning via Multi-Sample Hypernetworks
Pareto Front Learning (PFL) was recently introduced as an effective approach
to obtain a mapping function from a given trade-off vector to a solution on the
Pareto front, which solves the multi-objective optimization (MOO) problem. Due
to the inherent trade-off between conflicting objectives, PFL offers a flexible
approach in many scenarios in which the decision makers can not specify the
preference of one Pareto solution over another, and must switch between them
depending on the situation. However, existing PFL methods ignore the
relationship between the solutions during the optimization process, which
hinders the quality of the obtained front. To overcome this issue, we propose a
novel PFL framework namely PHN-HVI, which employs a hypernetwork to generate
multiple solutions from a set of diverse trade-off preferences and enhance the
quality of the Pareto front by maximizing the Hypervolume indicator defined by
these solutions. The experimental results on several MOO machine learning tasks
show that the proposed framework significantly outperforms the baselines in
producing the trade-off Pareto front.Comment: Accepted to AAAI-2
A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications
Pareto Front Learning (PFL) was recently introduced as an efficient method
for approximating the entire Pareto front, the set of all optimal solutions to
a Multi-Objective Optimization (MOO) problem. In the previous work, the mapping
between a preference vector and a Pareto optimal solution is still ambiguous,
rendering its results. This study demonstrates the convergence and completion
aspects of solving MOO with pseudoconvex scalarization functions and combines
them into Hypernetwork in order to offer a comprehensive framework for PFL,
called Controllable Pareto Front Learning. Extensive experiments demonstrate
that our approach is highly accurate and significantly less computationally
expensive than prior methods in term of inference time.Comment: Under Review at Neural Networks Journa
Music-Driven Group Choreography
Music-driven choreography is a challenging problem with a wide variety of
industrial applications. Recently, many methods have been proposed to
synthesize dance motions from music for a single dancer. However, generating
dance motion for a group remains an open problem. In this paper, we present
, a new large-scale dataset for music-driven group dance
generation. Unlike existing datasets that only support single dance, our new
dataset contains group dance videos, hence supporting the study of group
choreography. We propose a semi-autonomous labeling method with humans in the
loop to obtain the 3D ground truth for our dataset. The proposed dataset
consists of 16.7 hours of paired music and 3D motion from in-the-wild videos,
covering 7 dance styles and 16 music genres. We show that naively applying
single dance generation technique to creating group dance motion may lead to
unsatisfactory results, such as inconsistent movements and collisions between
dancers. Based on our new dataset, we propose a new method that takes an input
music sequence and a set of 3D positions of dancers to efficiently produce
multiple group-coherent choreographies. We propose new evaluation metrics for
measuring group dance quality and perform intensive experiments to demonstrate
the effectiveness of our method. Our project facilitates future research on
group dance generation and is available at:
https://aioz-ai.github.io/AIOZ-GDANCE/Comment: accepted in CVPR 202
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