Interactions between human and objects are influenced not only by the
object's pose and shape, but also by physical attributes such as object mass
and surface friction. They introduce important motion nuances that are
essential for diversity and realism. Despite advancements in recent
kinematics-based methods, this aspect has been overlooked. Generating nuanced
human motion presents two challenges. First, it is non-trivial to learn from
multi-modal human and object information derived from both the physical and
non-physical attributes. Second, there exists no dataset capturing nuanced
human interactions with objects of varying physical properties, hampering model
development. This work addresses the gap by introducing the FORCE model, a
kinematic approach for synthesizing diverse, nuanced human-object interactions
by modeling physical attributes. Our key insight is that human motion is
dictated by the interrelation between the force exerted by the human and the
perceived resistance. Guided by a novel intuitive physics encoding, the model
captures the interplay between human force and resistance. Experiments also
demonstrate incorporating human force facilitates learning multi-class motion.
Accompanying our model, we contribute the FORCE dataset. It features diverse,
different-styled motion through interactions with varying resistances.

通过模拟物体的物理属性，FORCE 模型引入了一种运动和真实感的多样性，以便细致地描绘人与物体之间的互动。

FORCE：直觉物理引导下人 - 物交互的数据集和方法

FORCE: Dataset and Method for Intuitive Physics Guided Human-object  Interaction

This paper investigates models of event implications. Specifically, how well
models predict entity state-changes, by targeting their understanding of
physical attributes. Nominally, Large Language models (LLM) have been exposed
to procedural knowledge about how objects interact, yet our benchmarking shows
they fail to reason about the world. Conversely, we also demonstrate that
existing approaches often misrepresent the surprising abilities of LLMs via
improper task encodings and that proper model prompting can dramatically
improve performance of reported baseline results across multiple tasks. In
particular, our results indicate that our prompting technique is especially
useful for unseen attributes (out-of-domain) or when only limited data is
available.

本文研究事件涵义的模型，通过探讨其对物理属性的理解能力来预测实体状态变化。作者发现，传统的大型语言模型无法很好地理解这方面的知识；而通过适当的提示方式，它们的性能可以得到极大的提升，尤其是对于未知的属性或数据信息不足的情况。

EvEntS ReaLM: 基于语言模型的实体状态事件推理

EvEntS ReaLM: Event Reasoning of Entity States via Language Models

Recent advances in generative adversarial networks (GANs) have led to
remarkable achievements in face image synthesis. While methods that use
style-based GANs can generate strikingly photorealistic face images, it is
often difficult to control the characteristics of the generated faces in a
meaningful and disentangled way. Prior approaches aim to achieve such semantic
control and disentanglement within the latent space of a previously trained
GAN. In contrast, we propose a framework that a priori models physical
attributes of the face such as 3D shape, albedo, pose, and lighting explicitly,
thus providing disentanglement by design. Our method, MOST-GAN, integrates the
expressive power and photorealism of style-based GANs with the physical
disentanglement and flexibility of nonlinear 3D morphable models, which we
couple with a state-of-the-art 2D hair manipulation network. MOST-GAN achieves
photorealistic manipulation of portrait images with fully disentangled 3D
control over their physical attributes, enabling extreme manipulation of
lighting, facial expression, and pose variations up to full profile view.

MOST-GAN 使用 3D 形态模型耦合现代化 2D 变形网络实现面部图像生成、控制和逼真操作。