1. concept of Video Texture
2. methods used to compute similarity between frames
3. use of similar frames to find transitions to generate Video Textures
4. Finding, Cutting, morphing for Video Textures
compute how similar f1 is to all frames f1, f2, f3 … f90
Compute the Euclidean distance between two frames
Consider two frames, p = {p1, p2, … pn} and q = {q1,q2,…qn}
Similar frames are the ones that would be best to jump to
preserving dynamics with transitions
Video portraits
1. Video stabilization
2. Estimating camera motion
3. smoothing camera paths
4. Rendering stabilized videos
5. Dealing with rolling shutter artifacts
original, stabilized
Optical / in-camera stabilization
– floating lens
– sensor shift
– accelerometer + Gyro
Post-process Stabilization
– removes low-frequency perturbations (large buffer)
1. estimate camera motion
2. stabilize camera path
3. crop and re-synthesize
Motion models translation
– translation in x and y
– 2 DOF
– Still very shaky
Path Smoothing
Goal: Approximate original path with stable one
tripod -> Constant segment
dolly or pan -> Linear segment
CCD vs. CMOS Sensors
amplifer
Difficulty: speed of readout varies across cameras
solution: use multiple motion models and blend using miztextures of Gaussians
