Event-based vision has drawn increasing attention due to its unique
characteristics, such as high temporal resolution and high dynamic range. It
has been used in video super-resolution (VSR) recently to enhance the flow
estimation and temporal alignment. Rather than for motion learning, we propose
in this paper the first VSR method that utilizes event signals for texture
enhancement. Our method, called EvTexture, leverages high-frequency details of
events to better recover texture regions in VSR. In our EvTexture, a new
texture enhancement branch is presented. We further introduce an iterative
texture enhancement module to progressively explore the
high-temporal-resolution event information for texture restoration. This allows
for gradual refinement of texture regions across multiple iterations, leading
to more accurate and rich high-resolution details. Experimental results show
that our EvTexture achieves state-of-the-art performance on four datasets. For
the Vid4 dataset with rich textures, our method can get up to 4.67dB gain
compared with recent event-based methods. Code:
this https URL

本研究提出了一种利用事件信号进行纹理增强的视频超分辨率方法，通过逐步探索高时间分辨率的事件信息以实现纹理修复，从而获得更精确和丰富的高分辨率细节，实验证明该方法在四个数据集上达到了最先进的性能。

EvTexture: 基于事件驱动的视频超分辨率纹理增强

EvTexture: Event-driven Texture Enhancement for Video Super-Resolution

Representing human performance at high-fidelity is an essential building
block in diverse applications, such as film production, computer games or
videoconferencing. To close the gap to production-level quality, we introduce
HumanRF, a 4D dynamic neural scene representation that captures full-body
appearance in motion from multi-view video input, and enables playback from
novel, unseen viewpoints. Our novel representation acts as a dynamic video
encoding that captures fine details at high compression rates by factorizing
space-time into a temporal matrix-vector decomposition. This allows us to
obtain temporally coherent reconstructions of human actors for long sequences,
while representing high-resolution details even in the context of challenging
motion. While most research focuses on synthesizing at resolutions of 4MP or
lower, we address the challenge of operating at 12MP. To this end, we introduce
ActorsHQ, a novel multi-view dataset that provides 12MP footage from 160
cameras for 16 sequences with high-fidelity, per-frame mesh reconstructions. We
demonstrate challenges that emerge from using such high-resolution data and
show that our newly introduced HumanRF effectively leverages this data, making
a significant step towards production-level quality novel view synthesis.

我们引入了 HumanRF，这是一种 4D 动态神经场景表示，可以捕获多视角视频输入中运动的全身外观，并允许从新的未见视角进行播放。通过分解时空为时间矩阵 - 向量分解，我们可以以高压缩比捕获高分辨率细节，并获得长序列的时间上连贯的演员重建。新的多视角数据集 ActorsHQ 提供高保真度的 16 个序列的 160 个相机的 12MP 镜头素材。我们的 HumanRF 有效利用了这些数据，在新颖视角合成方面取得了重要进展，实现接近于制作水平的品质。

HumanRF：高保真度的运动人体神经辐射场

HumanRF: High-Fidelity Neural Radiance Fields for Humans in Motion

In natural images, the scales (thickness) of object skeletons may
dramatically vary among objects and object parts, making object skeleton
detection a challenging problem. We present a new convolutional neural network
(CNN) architecture by introducing a novel hierarchical feature integration
mechanism, named Hi-Fi, to address the skeleton detection problem. The proposed
CNN-based approach has a powerful multi-scale feature integration ability that
intrinsically captures high-level semantics from deeper layers as well as
low-level details from shallower layers. % By hierarchically integrating
different CNN feature levels with bidirectional guidance, our approach (1)
enables mutual refinement across features of different levels, and (2)
possesses the strong ability to capture both rich object context and
high-resolution details. Experimental results show that our method
significantly outperforms the state-of-the-art methods in terms of effectively
fusing features from very different scales, as evidenced by a considerable
performance improvement on several benchmarks.

本论文提出了一种新的卷积神经网络架构，称为 Hi-Fi，并通过引入新的分层特征集成机制来解决对象骨架检测问题，在多种基准测试上取得了显着的性能提升，这表明了该方法的有效性。