Methods › General › Diffusion Models › LSDM

Language-driven Scene Synthesis using Multi-conditional Diffusion Model

LSDM

1 paper tagged archive 2025-07-28

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

Our main contribution is the Guiding Points Network, where we integrate all information from the conditions to generate guiding points. By applying transformation matrices to scene entities (human/objects) with attention weighting, we can forecast the spanning of the target object.

See Code · andvg3/LSDM

Papers archive 2025-07-28

1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

4 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Language Modelling1
Large Language Model1
Sentence1
knowledge editing1

Usage over time archive 2025-07-28

Papers per year tagged with LSDM: 2024 to 2024, peak 1 1 0 2024: 1 paper 2024
Papers per year the archive tags with this method, by the paper's archive date (1 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Diffusion Models3D Representations

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections