Precisely understanding how objects move in 3D is essential for broad scenarios such as video editing, gaming, driving, and athletics. With screen-displayed computer graphics content, users only perceive limited cues to judge the object motion from the on-screen optical flow. Conventionally, visual perception is studied with stationary settings and singular objects. However, in practical applications, we---the observer---also move within complex scenes. Therefore, we must extract object motion from a combined optical flow displayed on screen, which can often lead to mis-estimations due to perceptual ambiguities. We measure and model observers' perceptual accuracy of object motions in dynamic 3D environments, a universal but under-investigated scenario in computer graphics applications. We design and employ a crowdsourcing-based psychophysical study, quantifying the relationships among patterns of scene dynamics and content, and the resulting perceptual judgments of object motion direction. The acquired psychophysical data underpins a model for generalized conditions. We then demonstrate the model's guidance ability to significantly enhance users' understanding of task object motion in gaming and animation design. With applications in measuring and compensating for object motion errors in video and rendering, we hope the research establishes a new frontier for understanding and mitigating perceptual errors caused by the gap between screen-displayed graphics and the physical world.

中文翻译：

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评估动态环境中物体运动的视觉感知


精确了解对象在 3D 中的移动方式对于视频编辑、游戏、驾驶和田径等广泛场景至关重要。使用屏幕显示的计算机图形内容，用户只能感知有限的线索，从而从屏幕上的光流中判断物体的运动。传统上，视觉感知是通过静止的设置和单一的物体来研究的。然而，在实际应用中，我们---观察者---也在复杂的场景中移动。因此，我们必须从屏幕上显示的组合光流中提取物体运动，这通常会导致由于感知模糊而导致的错误估计。我们测量和建模观察者在动态 3D 环境中对物体运动的感知准确性，这是计算机图形应用中普遍但未得到充分研究的场景。我们设计并采用了一项基于众包的心理物理学研究，量化了场景动态和内容模式之间的关系，以及由此产生的对物体运动方向的感知判断。获得的心理物理学数据为一般病症的模型奠定了基础。然后，我们演示了该模型的指导能力，以显著增强用户在游戏和动画设计中对任务对象运动的理解。随着在视频和渲染中测量和补偿对象运动误差的应用，我们希望这项研究为理解和减轻由屏幕显示图形与物理世界之间的差距引起的感知误差建立一个新的领域。