Okay, here we go.
Neural Volume Rendering for Dynamic Scenes
NeRF has shown incredible view synthesis results, but it requires multi-view captures for STATIC scenes.
How can we achieve view synthesis for DYNAMIC scenes from a single video? Here is what I learned from several recent efforts.
Okay, here we go.
NeRF represents the scene as a 5D continuous volumetric scene function that maps the spatial position and viewing direction to color and density. It then projects the colors/densities to form an image with volume rendering.
Volumetric + Implicit -> Awesome!
Building on NeRF, one can extend it for handling dynamic scenes with two types of approaches.
A) 4D (or 6D with views) function.
One direct approach is to include TIME as an additional input to learn a DYNAMIC radiance field.
e.g., Video-NeRF, NSFF, NeRFlow
Inspired by non-rigid reconstruction methods, this type of approach learns a radiance field in a canonical frame (template) and predicts deformation for each frame to account for dynamics over time.
e.g., Nerfie, NR-NeRF, D-NeRF
All the methods use an MLP to encode the deformation field. But, how do they differ?
A) INPUT: How to encode the additional time dimension as input?
B) OUTPUT: How to parametrize the deformation field?
One can choose to use EXPLICIT conditioning by treating the frame index t as input.
Alternatively, one can use a learnable LATENT vector for each frame.
We can either use the MLP to predict
- dense 3D translation vectors (aka scene flow) or
- dense rigid motion field
More from Tech
BREAKING: @CommonsCMS @DamianCollins just released previously sealed #Six4Three @Facebook documents:
Some random interesting tidbits:
1) Zuck approves shutting down platform API access for Twitter's when Vine is released #competition
2) Facebook engineered ways to access user's call history w/o alerting users:
Team considered access to call history considered 'high PR risk' but 'growth team will charge ahead'. @Facebook created upgrade path to access data w/o subjecting users to Android permissions dialogue.
3) The above also confirms @kashhill and other's suspicion that call history was used to improve PYMK (People You May Know) suggestions and newsfeed rankings.
4) Docs also shed more light into @dseetharaman's story on @Facebook monitoring users' @Onavo VPN activity to determine what competitors to mimic or acquire in 2013.
https://t.co/PwiRIL3v9x
Some random interesting tidbits:
1) Zuck approves shutting down platform API access for Twitter's when Vine is released #competition
2) Facebook engineered ways to access user's call history w/o alerting users:
Team considered access to call history considered 'high PR risk' but 'growth team will charge ahead'. @Facebook created upgrade path to access data w/o subjecting users to Android permissions dialogue.
3) The above also confirms @kashhill and other's suspicion that call history was used to improve PYMK (People You May Know) suggestions and newsfeed rankings.
4) Docs also shed more light into @dseetharaman's story on @Facebook monitoring users' @Onavo VPN activity to determine what competitors to mimic or acquire in 2013.
https://t.co/PwiRIL3v9x