10 research outputs found
Stable Score Distillation for High-Quality 3D Generation
Although Score Distillation Sampling (SDS) has exhibited remarkable
performance in conditional 3D content generation, a comprehensive understanding
of its formulation is still lacking, hindering the development of 3D
generation. In this work, we decompose SDS as a combination of three functional
components, namely mode-seeking, mode-disengaging and variance-reducing terms,
analyzing the properties of each. We show that problems such as over-smoothness
and implausibility result from the intrinsic deficiency of the first two terms
and propose a more advanced variance-reducing term than that introduced by SDS.
Based on the analysis, we propose a simple yet effective approach named Stable
Score Distillation (SSD) which strategically orchestrates each term for
high-quality 3D generation and can be readily incorporated to various 3D
generation frameworks and 3D representations. Extensive experiments validate
the efficacy of our approach, demonstrating its ability to generate
high-fidelity 3D content without succumbing to issues such as over-smoothness
SimCalib: Graph Neural Network Calibration based on Similarity between Nodes
Graph neural networks (GNNs) have exhibited impressive performance in
modeling graph data as exemplified in various applications. Recently, the GNN
calibration problem has attracted increasing attention, especially in
cost-sensitive scenarios. Previous work has gained empirical insights on the
issue, and devised effective approaches for it, but theoretical supports still
fall short. In this work, we shed light on the relationship between GNN
calibration and nodewise similarity via theoretical analysis. A novel
calibration framework, named SimCalib, is accordingly proposed to consider
similarity between nodes at global and local levels. At the global level, the
Mahalanobis distance between the current node and class prototypes is
integrated to implicitly consider similarity between the current node and all
nodes in the same class. At the local level, the similarity of node
representation movement dynamics, quantified by nodewise homophily and relative
degree, is considered. Informed about the application of nodewise movement
patterns in analyzing nodewise behavior on the over-smoothing problem, we
empirically present a possible relationship between over-smoothing and GNN
calibration problem. Experimentally, we discover a correlation between nodewise
similarity and model calibration improvement, in alignment with our theoretical
results. Additionally, we conduct extensive experiments investigating different
design factors and demonstrate the effectiveness of our proposed SimCalib
framework for GNN calibration by achieving state-of-the-art performance on 14
out of 16 benchmarks
Enhancing Expressiveness in Dance Generation via Integrating Frequency and Music Style Information
Dance generation, as a branch of human motion generation, has attracted
increasing attention. Recently, a few works attempt to enhance dance
expressiveness, which includes genre matching, beat alignment, and dance
dynamics, from certain aspects. However, the enhancement is quite limited as
they lack comprehensive consideration of the aforementioned three factors. In
this paper, we propose ExpressiveBailando, a novel dance generation method
designed to generate expressive dances, concurrently taking all three factors
into account. Specifically, we mitigate the issue of speed homogenization by
incorporating frequency information into VQ-VAE, thus improving dance dynamics.
Additionally, we integrate music style information by extracting genre- and
beat-related features with a pre-trained music model, hence achieving
improvements in the other two factors. Extensive experimental results
demonstrate that our proposed method can generate dances with high
expressiveness and outperforms existing methods both qualitatively and
quantitatively