3D Moments from Near-Duplicate Photos
원문 초록
We introduce 3D Moments, a new computational photography effect. As input we take a pair of near-duplicate photos, i.e., photos of moving subjects from similar viewpoints, common in people's photo collections. As output, we produce a video that smoothly interpolates the scene motion from the first photo to the second, while also producing camera motion with parallax that gives a heightened sense of 3D. To achieve this effect, we represent the scene as a pair of feature-based layered depth images augmented with scene flow. This representation enables motion interpolation along with independent control of the camera viewpoint. Our system produces photorealistic space-time videos with motion parallax and scene dynamics, while plausibly recovering regions occluded in the original views. We conduct extensive experiments demonstrating superior performance over baselines on public datasets and in-the-wild photos. Project page: https://3d-moments.github.io/
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
MoMask: Generative Masked Modeling of 3D Human Motions
MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-Resolution
Continuous Intermediate Token Learning with Implicit Motion Manifold for Keyframe Based Motion Interpolation
MoDi: Unconditional Motion Synthesis from Diverse Data
MotionCLIP: Exposing Human Motion Generation to CLIP Space
Neural 3D Video Synthesis from Multi-view Video
토론
이 논문과 연결된 포럼 글이 아직 없습니다.