SIGNER: Temporally Grounded Sign Language Generation via Time-Resolved Conditioning

Sign language generation (SLG), also known as text-to-signgeneration, aims to bridge the communication gap between signers andnon-signers. Unlike many other generative tasks, SLG must satisfy twofundamental linguistic constraints. First, sign language expresses mean-ing through a sequence of gestures aligned with word-like units calledglosses, and therefore requires correct lexical ordering to preserve in-tended meaning. Second, each gesture should faithfully reflect the in-tended gloss (semantic accuracy). Despite recent progress, existing SLGmethods frequently produce signs with incorrect lexical order and lowsemantic accuracy. A common limitation of prior approaches stems fromglobally fused conditioning strategies, which weaken temporal ground-ing, the temporal correspondence between glosses and their realized signsegments. This often leads to incorrect lexical order and semanticallyambiguous signs. To address this limitation, we propose SIGNER, aSIGN language generation framework with timE-Resolved conditioningto ensure temporal grounding, leveraging a temporal-gloss condition andlocal temporal fusion (LTF). SIGNER constructs a temporal-gloss con-dition by estimating a gloss sequence and its durations from input text,and assigning gloss semantics across the temporal dimension. We thenintroduce LTF, a temporally grounded fusion module that integratesthe temporal-gloss condition within a constrained temporal window dur-ing denoising. By enforcing temporal locality in condition fusion, LTFpreserves temporal grounding, leading to correct lexical ordering andclearer per-gloss semantics. Experiments on Phoenix-2014T and CSL-Daily demonstrate state-of-the-art performance, further supported bymotion-smoothness analysis. The project page is available here.