Style transfer for out-of-domain (OOD) singing voice synthesis (SVS) focuses
on generating high-quality singing voices with unseen styles (such as timbre,
emotion, pronunciation, and articulation skills) derived from reference singing
voice samples. However, the endeavor to model the intricate nuances of singing
voice styles is an arduous task, as singing voices possess a remarkable degree
of expressiveness. Moreover, existing SVS methods encounter a decline in the
quality of synthesized singing voices in OOD scenarios, as they rest upon the
assumption that the target vocal attributes are discernible during the training
phase. To overcome these challenges, we propose StyleSinger, the first singing
voice synthesis model for zero-shot style transfer of out-of-domain reference
singing voice samples. StyleSinger incorporates two critical approaches for
enhanced effectiveness: 1) the Residual Style Adaptor (RSA) which employs a
residual quantization module to capture diverse style characteristics in
singing voices, and 2) the Uncertainty Modeling Layer Normalization (UMLN) to
perturb the style attributes within the content representation during the
training phase and thus improve the model generalization. Our extensive
evaluations in zero-shot style transfer undeniably establish that StyleSinger
outperforms baseline models in both audio quality and similarity to the
reference singing voice samples. Access to singing voice samples can be found
at this https URL

StyleSinger 是首个用于零样本风格转移的领域外参考唱声样本的歌声合成模型，通过采用剩余风格适应器和不确定性建模层标准化的关键方法，它在音频质量和与参考唱声样本的相似性方面优于基线模型。

StyleSinger: 跨领域歌声合成的风格转换

StyleSinger: Style Transfer for Out-Of-Domain Singing Voice Synthesis

3D style transfer aims to render stylized novel views of a 3D scene with
multi-view consistency. However, most existing work suffers from a three-way
dilemma over accurate geometry reconstruction, high-quality stylization, and
being generalizable to arbitrary new styles. We propose StyleRF (Style Radiance
Fields), an innovative 3D style transfer technique that resolves the three-way
dilemma by performing style transformation within the feature space of a
radiance field. StyleRF employs an explicit grid of high-level features to
represent 3D scenes, with which high-fidelity geometry can be reliably restored
via volume rendering. In addition, it transforms the grid features according to
the reference style which directly leads to high-quality zero-shot style
transfer. StyleRF consists of two innovative designs. The first is
sampling-invariant content transformation that makes the transformation
invariant to the holistic statistics of the sampled 3D points and accordingly
ensures multi-view consistency. The second is deferred style transformation of
2D feature maps which is equivalent to the transformation of 3D points but
greatly reduces memory footprint without degrading multi-view consistency.
Extensive experiments show that StyleRF achieves superior 3D stylization
quality with precise geometry reconstruction and it can generalize to various
new styles in a zero-shot manner.

提出了一种具有更高 3D 风格转移质量的 StyleRF 技术，该技术在辐射场的特征空间内进行样式转换，实现了准确的几何重建、高质量的风格化和具有拓展性的任意新样式的生成。

StyleRF: 零样本 3D 神经辐射场风格迁移

StyleRF: Zero-shot 3D Style Transfer of Neural Radiance Fields

Style transfer for out-of-domain (OOD) speech synthesis aims to generate
speech samples with unseen style (e.g., speaker identity, emotion, and prosody)
derived from an acoustic reference, while facing the following challenges: 1)
The highly dynamic style features in expressive voice are difficult to model
and transfer; and 2) the TTS models should be robust enough to handle diverse
OOD conditions that differ from the source data. This paper proposes
GenerSpeech, a text-to-speech model towards high-fidelity zero-shot style
transfer of OOD custom voice. GenerSpeech decomposes the speech variation into
the style-agnostic and style-specific parts by introducing two components: 1) a
multi-level style adaptor to efficiently model a large range of style
conditions, including global speaker and emotion characteristics, and the local
(utterance, phoneme, and word-level) fine-grained prosodic representations; and
2) a generalizable content adaptor with Mix-Style Layer Normalization to
eliminate style information in the linguistic content representation and thus
improve model generalization. Our evaluations on zero-shot style transfer
demonstrate that GenerSpeech surpasses the state-of-the-art models in terms of
audio quality and style similarity. The extension studies to adaptive style
transfer further show that GenerSpeech performs robustly in the few-shot data
setting. Audio samples are available at this https URL

GenerSpeech 是一种用于处理 out-of-domain (OOD) 语音合成的文本到语音模型，具有高保真的零样式迁移能力。它通过两个组件将语音变化分解为样式不可知和样式特定部分，以提高模型的泛化能力，并在零样式迁移和自适应样式迁移方面超过了现有技术。