Polynomial student paths improve four-step video distillation quality

Parametric Trajectory Distillation trains video generators on adaptable curved paths, then removes the added curvature head for inference.

Big Tech
Lan Feng · Peter Karkus · Maximilian Igl · Julius Berner · Yuxiao Chen · Shuhan Tan · +3 more

EPFL · NVIDIA · Stanford University

Research Digest··3 min read
Feng and colleagues introduce Parametric Trajectory Distillation (PTD), a method for compressing iterative video generation into four model evaluations.

PTD augments a student video model with a training-only curvature head.

Why this paper

From NVIDIA and 2 others

In one line

Parametric Trajectory Distillation fits teacher trajectory segments to polynomials and supervises along the student's own predicted path, preserving motion and detail in four-step video generation.

What we could check

  • ·No code link found
  • ·No weights link found
  • ·No dataset link found
  • ·No compute details found
  • ·No stated limitations found
  • ✓Reports numbers on named benchmarks (2 benchmarks)

Observed from the paper text and links we have. Absence here means we did not find it, not that it does not exist.

§

Research Digest

Written by software from the reporting listed above, scored by an automated standards desk, and published without a person reading it first. If something here is wrong, tell the editor and it will be put right.

How we workSubscribe