r - Back transform mixed-effects model's regression coefficients for fixed-effects from log to original scale -

i running mixed-effects model lme4 package. model specifications are: log(dv) ~ 1 + a*b*c + (1+a*b|random1) + (1+a|random2), a , b within-group conditions , c between-group condition.

the first problem coefficients fixed effects on log scale , intercept makes sense when exp(coef) (see below).

the second problem if exponentiation transform, how should account random-effects structure? understand it, random-effects structure affects fixed-effects coefficients (i might wrong here).

this sample output of fixed-effects coefficients:

            estimate (intercept) 6.533079 a1          0.062171 a2          0.077409 b1         -0.184366 b2         -0.154115 c           0.152238 a1:b1      -0.015494 a2:b1      -0.017655 a1:b2       0.001674 a2:b2      -0.003641 a1:c        0.013021 a2:c        0.038995 b1:c        0.010087 b2:c        0.013721 a1:b1:c     0.016025 a2:b1:c     0.016453 a1:b2:c     0.012746 a2:b2:c     0.003113 

now, exp(6.533079) gives 687.5118, makes sense in original scale, rest of numbers not make sense once transformed.