  The emergence of text-driven motion synthesis technique provides animators
with great potential to create efficiently. However, in most cases, textual
expressions only contain general and qualitative motion descriptions, while
lack fine depiction and sufficient intensity, leading to the synthesized
motions that either (a) semantically compliant but uncontrollable over specific
pose details, or (b) even deviates from the provided descriptions, bringing
animators with undesired cases. In this paper, we propose DiffKFC, a
conditional diffusion model for text-driven motion synthesis with KeyFrames
Collaborated, enabling realistic generation with collaborative and efficient
dual-level control: coarse guidance at semantic level, with only few keyframes
for direct and fine-grained depiction down to body posture level. Unlike
existing inference-editing diffusion models that incorporate conditions without
training, our conditional diffusion model is explicitly trained and can fully
exploit correlations among texts, keyframes and the diffused target frames. To
preserve the control capability of discrete and sparse keyframes, we customize
dilated mask attention modules where only partial valid tokens participate in
local-to-global attention, indicated by the dilated keyframe mask.
Additionally, we develop a simple yet effective smoothness prior, which steers
the generated frames towards seamless keyframe transitions at inference.
Extensive experiments show that our model not only achieves state-of-the-art
performance in terms of semantic fidelity, but more importantly, is able to
satisfy animator requirements through fine-grained guidance without tedious
labor.
